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Integrating AI into assignments

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Here we offer strategies and perspectives on integrating AI tools into assignments and activities used to assess student learning.

Creating your course policy on AI

  • An effective syllabus works to motivate learning, define goals, explain course structure, and provide support to students as they learn.
  • Be clearly stated and specific
  • Clarify the context or conditions of allowable AI use
  • Explain processes and consequences for non-compliance
  • Have a thoughtful pedagogic rationale in support of student learning
  • Connect to support resources
  • Show support for student well-being

Outcomes for this module

In this module, we will analyze activities and assignments used for assessing learning, provide student-centered perspectives, and offer strategies for developing assessment activities and assignments that integrate student use of generative AI chatbots.

After completing this module, you should be able to:

  • Describe why your assessment activities are meaningful to learners.
  • Identify and clarify the learning objectives of your assessment activities.
  • Identify relevant strategies that can be applied to assessment activities in your course.
  • Empathize with student perspectives on using AI in course assessment activities.

Warm-up with a metacognitive exercise

As you begin to explore, think about what you already know and the opinions you may already hold about the educational aspects of AI chatbots. This metacognitive exercise can help you identify what you want to explore and what you already understand. Making connections to what you already know can deepen your learning and support your engagement with these modules.

Begin with the prompt, “Describe an assignment or assessment activity that integrated technology in a way that was effective and engaging for your learning,” and respond to the poll below.

Unpacking your assessment activities and assignments

When designing or adapting an activity or assignment used to assess learning, whether you integrate AI or not, we encourage you to consider two questions: why is this meaningful, and what are students supposed to learn from it?

Define why it is meaningful

Students can learn better when they are motivated and can make meaningful connections to coursework (Headden & McKay, 2015). We might assume that students’ motivations focus on their grades, but that assumption does not provide the full picture, and when applied in isolation it is not likely to sustain deep learning. Articulating what makes an activity meaningful, motivational, and memorable for students can help you create an engaging activity or assignment that enhances student learning and motivation.

Concerning AI chatbots, perhaps the activity or assignment addresses AI in ways that prepare students for future careers, enhance their social connections, or touch upon broader issues they care about. We encourage you to talk with your students about what they find meaningful to inform the design of your activities and assignments. What leads them to want to engage?

Also, reflect on why the assignment is meaningful to you. Is it simply convenient to implement (and standard in your experience as a student and teacher) or does it connect to something deeper in your pedagogy? Perhaps the assignment reinforces the norms and values that you share with other professionals in your discipline, allows you to connect with students in more meaningful ways, builds foundational skills for other parts of the curricula, or explores emergent opportunities and challenges with AI for your field.

Define what students are intended to learn

Next, identify and clarify the underlying learning objectives of the assignment or activity. The objective should describe the observable skills or behaviors students will have learned to perform after completing the activity. Clearly articulated learning objectives can help you develop activities that support learning and assessments that accurately measure student learning.

When thinking about AI chatbots and how they impact writing, you might ask yourself, “What are the underlying learning objectives being addressed through writing?” Instructors may assign writing tasks to assess how students engage with content. In the past, teachers could assume with good reason that a student producing coherent writing must have engaged with the content to generate writing that makes sense. However, we might also question this assumption about the automatic connection between coherent writing and deep engagement. The advent of generative AI has certainly exacerbated this.

Do you ask your students to write to demonstrate and reinforce content knowledge? Do they write to analyze and critique a position? Do they write to formulate arguments and cite evidence? Do they write as a form of creative expression? When you think about the available options, you can likely develop many ways for students to learn and demonstrate these skills with or without writing. Ultimately, honing in on the underlying learning objectives can help you integrate generative AI tools into an assignment.

Students can benefit from understanding how AI works and the educational opportunities and challenges that it presents. Consider offering the content in the modules in this guide to your students as supplemental reading or as part of a class activity.

Strategies for implementing AI into activities and assignments

As you think through how you might address or integrate AI tools in an assessment activity or assignment, we encourage you to consider a range of possibilities related to the specific aims of your course and the needs of your students. Here we offer a variety of pedagogical strategies for you to consider. We present these strategies in the context of students using AI chatbots, but they also apply to contexts without AI. Remember why your assignment is meaningful in relation to your learning objectives to help you select appropriate strategies.

Leverage multiple modalities

Consider ways to diversify when and where you assess student learning and the formats students use to express what they’ve learned.

Use more in-class assignments

Strategies like the flipped classroom model assign lecture content as homework and use the in-class time for learning activities (Lage et al., 2000). You can use this in-class time to integrate more low-stakes assessment activities during which you can better guide students toward using AI in ways that support learning.

Multiple modes of expression

Students may differ in how they can best articulate what they know. Using multiple modalities of expression, such as having students complete assignments that require speaking or graphic representations instead of only written text, stands out as an established strategy within the Universal Design for Learning framework that could apply here. While chatbots primarily generate written text, other AI tools can generate music, graphics, and video. You can thus create assessment activities that integrate multiple modalities at once.

For example, if you are assessing students’ understanding of cultural exchange in the ancient world, students might create a mind map or timeline to visually represent important trends, events, or concepts covered in the assigned readings. AI might then be used to generate images of artifacts, portraits, or cityscapes based on historical descriptions.

Make grading practices clear

Consider ways to clarify for students how they are being graded and what is expected of them.

Require robust citation

Have students learn about and adopt more robust citation practices, especially if they use AI tools for writing. You might begin with conversations about what plagiarism entails and why ethics matter in higher education and your discipline. Then connect students to resources on citation and documentation .

If you and your students decide to use AI tools, you can find style guidelines about citing AI-generated text for APA style and MLA style . These guidelines advise writers to cite the AI tool whenever they paraphrase, quote, or incorporate AI-generated content, acknowledge how they used the tool (for brainstorming, editing, and so on), and vet secondary sources generated by AI. For example, students could include citations for AI in the Works Cited section of their work and also include a statement describing why and how they used AI chatbots.

Establish and communicate clear assessment criteria

Try to bring assessment activities, learning objectives, and evaluation criteria into alignment. For example, if your objectives and assessments center around students proposing a solution to an open-ended problem, then the evaluation criteria might touch upon the feasibility, impact, or comprehensiveness of the proposed solutions. The criteria can vary a lot depending on your content and course, but your students benefit when you communicate these criteria and the purpose and reasoning behind them (Allen & Tanner, 2006).

For example, when integrating AI chatbots into a writing task for students, you might put more weight on the quality of their ideas and the validity of cited sources and less weight on structure, grammar, and word choice. You might then create a rubric that you discuss with students in advance so they have a clear understanding of what will guide you in assessing their work.

Assess learning throughout the course

Consider ways to assess student learning throughout your course as opposed to assessing mostly at the end of the course.

Emphasize the process

You may be able to more effectively assess student learning during the different stages of the process as opposed to assessing learning based on their finished work (Xu, Shen, Islam, et al., 2023). Whether or not students use AI tools, they can benefit from segmenting a large project into smaller components with multiple opportunities for feedback and revision. Also, consider how you might adjust grading criteria or grade weights to put more emphasis on the process.

For some steps in the thinking process, such as brainstorming ideas, formulating a position, and outlining a solution, allowing students to use AI tools might benefit their process. For example, you might have students begin with low-stakes free-writing, such as brainstorming, then use AI chatbots to explore possible areas for further investigation based on the ideas students generate through their exploratory writing. Students might then critique and revise the AI-generated ideas into an outline.

Leverage formative feedback

Teachers provide formative feedback to students throughout the learning process to stimulate growth and improvement. Formative feedback can help students identify misunderstandings, reinforce desirable practices, and sustain motivation (Wylie et al., 2012). You and the teaching team might provide feedback directly to students or you might facilitate students giving feedback to each other. You might then assess how students follow up on feedback they receive.

You can use AI tools to inform your feedback to students or generate feedback directly for students. AI tools could provide instant, individualized feedback efficiently and frequently, supplementing the feedback provided by your teaching team. For example, you might share your existing assignment, rubric, and sample feedback with the chatbot and give it instructions on when and how to give feedback. Importantly, you should review feedback generated by chatbots for accuracy and relevance. Refine and save the prompts that work best. You might later share the prompts you’ve developed with students so they may use them to generate feedback themselves.

Make assignments more meaningful

Consider how you might make your assignments more relatable and meaningful to your students.

Personalize assessments

When done thoughtfully, connecting assessments to the personal experiences, identities, and concerns of students and their communities can help to motivate and deepen learning (France, 2022). You might also connect assignments to contexts specific to Stanford, your course, or your specific group of students.

With AI, you or your students might generate practice questions on topics that came up during a specific class discussion or generate analogies for complex concepts based on their interests and backgrounds. You might ground an assessment activity in local contexts, such as having your engineering students propose a plan to improve Lake Lagunita.

Use real-world assessment tasks

Assignments that leverage real-world problems, stakeholders, and communities that students are likely to engage with in their work lives can be motivational and valid ways of evaluating a student’s skills and knowledge (Sambell et al., 2019).

For example, students might work with real (or AI-simulated) business or community partners to develop a prototype product or policy brief. Students might have more time to work with those stakeholders and refine their proposal concepts if they can use AI tools to assist with time-consuming tasks, such as summarizing interview transcripts, writing a project pitch statement, or generating concept images.

AI itself might provide a relevant topic of study for your course. For example, you might examine AI as part of a discussion in a course about copyright and intellectual property law. Or you might analyze AI companies such OpenAI or Anthropic as case studies in a business course.

Assess more advanced learning

Consider ways you might assess more advanced or wider-ranging learning goals and objectives.

Emphasize metacognitive reflection

Metacognitive reflection activities, where students think about what and how they learn, can help students improve their learning (Velzen, 2017). You might use polls, discussion activities, or short writing exercises through which students identify what they already know about the topic, what they learned, what questions remain, and what learning strategies they might use for studying.

AI chatbots can help guide the reflection process like this reflection tool being developed by Leticia Britos Cavagnaro at Stanford d.school . Or perhaps students complete some activities with AI, then reflect on how it benefits or hinders their learning, and what strategies they might use to best leverage AI for learning.

Prioritize higher-order thinking

While students should develop mastery over foundational skills such as understanding concepts, identifying key characteristics, and recalling important information, practicing higher-order thinking skills, such as solving complex problems, creating original works, or planning a project, can deepen learning. For example, you might frame student essays as a defense of their views rather than a simple presentation of content knowledge. You might adjust assessment criteria to prioritize creativity or applying skills to new contexts.

Prioritizing higher-order thinking can encourage students to use AI tools to go beyond simply generating answers to engaging deeply with AI chatbots to generate sophisticated responses. Students could conduct preliminary research to find reliable sources that verify or refute the claims made by the AI chatbots. AI chatbots might then generate feedback, provide prompts for further reflection, or simulate new contexts.

Putting it all together

Here we offer a practical example: first, a typical assignment as usually designed, and then how you could enhance the assignment with some strategies that integrate AI chatbots.

When thinking about your course, start with small changes to one assignment and steadily expand upon them. Try to use AI chatbots for your other work tasks to build your fluency. Talk with students and colleagues about how the changes to your course work out concerning student engagement and learning. When integrating AI into an existing assignment, begin with an assignment that already has clearly defined learning objectives and rationale. Begin by using AI or other technology to supplement existing parts of the process of completing the assignment.

More examples of AI assignments

  • AI Pedagogy Project from metaLAB (at) Harvard
  • Exploring AI Pedagogy from the MLA-CCCC Joint Task Force on Writing and AI
  • TextGenEd: Continuing Experiments, January 2024 Collection from WAC Clearinghouse

Example of an assignment without AI

Currently, your students in an epidemiology course write essays summarizing the key concepts of an academic article about the socio-determinants of diabetes . This assessment activity has meaning because it focuses on a foundational concept students need to understand for later public health and epidemiology courses. The learning objective asks students to describe why socio-economic status is a strong predictor for certain diseases. Students write a five-page essay about a disease that can be predicted by socio-economic status including at least three additional citations. Students complete the essay, which counts for 30% of the final grade, before the final exam.

An example of an assignment that integrates AI

Using some of the strategies in the above sections, you might redesign this assignment to integrate the use of AI chatbots. Keep in mind that you would likely make small changes to a major assignment over multiple quarters. Consider some of the ideas below.

A meaningful assignment

The redesigned assessment activity carries more meaning to students because they might have personal experience of some communities adversely affected by these kinds of diseases, and public health issues like this intersect with other social injustices that students have expressed concern about.

Learning objectives

The objectives of the assessment activity include that students will be able to:

  • Describe how this disease affects particular communities or demographics
  • Explain the difference between correlation and causality regarding socioeconomic status and the disease
  • Propose a public health intervention that could help to address this issue

Assignment elements with AI

Students generate explanations of medical terminology in the selected articles to aid with reading comprehension. They generate several analogies for the core concept that apply to their own life experiences and communities. Students share these analogies in a Canvas forum graded for participation. Instructors provide general feedback in class.

Informed by the article, students then prompt a chatbot with biographical stories for two fictional characters from communities they care about incorporating differing socio-economic factors. Then they guide the chatbot in generating a dialogue or short story that illustrates how the two characters could have different health outcomes that might correlate with their socio-economic status. Students might use AI image generators for illustrations to accompany their stories. Students submit the work via Canvas for evaluation; the teacher shares exemplars in class.

Using an AI chatbot prompt provided by the instructor, students explore possible ideas for public health interventions. The provided prompt instructs the chatbot only to help students develop their ideas rather than suggesting solutions to them. With the aid of the chatbot, the students develop a public health intervention proposal.

Assignment elements without AI

Students discuss the differences between correlation and causation, critically analyze the generated characters and stories, and address any biases and stereotypes that surfaced during the activity. You facilitate the discussion with prompts and guidelines you developed with the aid of AI chatbots. Students write an in-class metacognitive reflection that you provide feedback on and grade for completion.

Students draw posters that summarize their proposed intervention. They critique and defend their proposals in a classroom poster session. Students complete a peer evaluation form for classmates. You evaluate the posters and their defenses with a grading rubric that you developed with the aid of an AI chatbot.

Students write an in-class reflection on their projects summarizing what they have learned over the length of the project, how the activities aided their learning, and so on. This is submitted to Canvas for grading and evaluation.

Student-centered perspective on using AI for learning

When thinking about integrating generative AI into a course assignment for students, we should consider some underlying attitudes that we, the authors, hold as educators, informed by our understanding of educational research on how people learn best. They also align with our values of inclusion, compassion, and student-centered teaching. When thinking through ways to integrate AI into a student assignment, keep the following perspectives in mind.

AI is new to students too

Like many of us, students likely have a wide range of responses to AI. Students may feel excited about how AI can enhance their learning and look for opportunities to engage with it in their classes. They may have questions about course policies related to AI use, concerns about how AI impacts their discipline or career goals, and so on. You can play a valuable role in modeling thoughtful use of AI tools and helping students navigate the complex landscape of AI.

Work with students, not against them

You and your students can work together to navigate these opportunities and challenges. Solicit their perspectives and thoughts about AI. Empower students to have agency over their learning and to think about AI and other technologies they use. Teaching and learning are interconnected and work best in partnership. Approach changes to your teaching and course to empower all students as literate, responsible, independent, and thoughtful technology users.

Look at AI and students in a positive light

Education as a discipline has repeatedly integrated new technologies that may have seemed disruptive at first. Educators and students typically grapple with new technology as they determine how to best leverage its advantages and mitigate its disadvantages. We encourage you to maintain a positive view of student intentions and the potential of AI tools to enhance learning. As we collectively discover and develop effective practices, we encourage you to maintain a positive and hopeful outlook. We should try to avoid assuming that most students would use generative AI in dishonest ways or as a shortcut to doing course assignments just because some students might behave this way.

Assess and reinforce your learning

We offer this activity for you to self-assess and reflect on what you learned in this module.

Stanford affiliates

  • Go to the Stanford-only version of this activity
  • Use your Stanford-provided Google account to respond.
  • You have the option of receiving an email summary of your responses.
  • After submitting your responses, you will have the option to view the anonymized responses of other Stanford community members by clicking Show previous responses .

Non-Stanford users

  • Complete the activity embedded below.
  • Your responses will only be seen by the creators of these modules.
  • Course and Assignment (Re-)Design , University of Michigan, Information and Technology Services
  • ChatGPT Assignments to Use in Your Classroom Today , University of Central Florida

Works Cited

Allen, D., and Tanner, K. (2006). Rubrics: Tools for Making Learning Goals and Evaluation Criteria Explicit for Both Teachers and Learners. CBE - Life Sciences Education. 5(3): 197-203.

Ashford-Rowe, K., Herrington, J., & Brown, C. (2014). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education, 39. https://doi.org/10.1080/02602938.2013.819566  

Bijlsma-Rutte, A., Rutters, F., Elders, P. J. M., Bot, S. D. M., & Nijpels, G. (2018). Socio-economic status and HbA1c in type 2 diabetes: A systematic review and meta-analysis. Diabetes/Metabolism Research and Reviews, 34(6), e3008. https://doi.org/10.1002/dmrr.3008  

CAST. (n.d.). UDL: The UDL Guidelines. Retrieved January 22, 2024, from https://udlguidelines.cast.org/  

Exploring AI Pedagogy. (n.d.). A Community Collection of Teaching Reflections. Retrieved January 22, 2024, from https://exploringaipedagogy.hcommons.org/  

France, P. E. (2022). Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms (2nd ed.). Corwin.

Headden, S., & McKay, S. (2015). Motivation Matters: How New Research Can Help Teachers Boost Student Engagement. Carnegie Foundation for the Advancement of Teaching. https://eric.ed.gov/?id=ED582567  

Hume Center for Writing and Speaking. (n.d.). Documentation and Citation. Retrieved January 22, 2024, from https://hume.stanford.edu/resources/student-resources/writing-resources…  

Lage, M. J., Platt, G. J., & Treglia, M. T. (2000). Inverting the Classroom: A gateway to creating an inclusive learning environment. Journal of Economic Education, 31(1), 30-43.

metaLAB (at) Harvard. (n.d.). The AI Pedagogy Project. Retrieved January 22, 2024, from https://aipedagogy.org/  

MLA Style Center. (2023, March 17). How do I cite generative AI in MLA style? https://style.mla.org/citing-generative-ai/  

Office of Community Standards. (n.d.). What Is Plagiarism? Retrieved January 22, 2024, from https://communitystandards.stanford.edu/policies-guidance/bja-guidance-…  

Sambell, K., Brown, S., & Race, P. (2019). Assessment to Support Student Learning: Eight Challenges for 21st Century Practice. All Ireland Journal of Higher Education, 11(2), Article 2. https://ojs.aishe.org/index.php/aishe-j/article/view/414  

The WAC Clearinghouse. (n.d.). January 2024. Retrieved January 22, 2024, from https://wac.colostate.edu/repository/collections/continuing-experiments…  

U-M Generative AI. (n.d.). Course and Assignment (Re-)Design. Retrieved January 22, 2024, from https://genai.umich.edu/guidance/faculty/redesigning-assessments  

Van Velzen, J. (2017). Metacognitive Knowledge: Development, Application, and Improvement. Information Age Publishing. https://content.infoagepub.com/files/fm/p599a21e816eb6/9781641130240_FM… . ISBN 9781641130226. 

Wylie, E. C., Gullickson, A. R., Cummings, K. E., Egelson, P., Noakes, L. A., Norman, K. M., Veeder, S. A., ... Popham, W. J. (2012). Improving Formative Assessment Practice to Empower Student Learning. Corwin Press.

Xu, X., Shen, W., Islam, A. A., et al. (2023). A whole learning process-oriented formative assessment framework to cultivate complex skills. Humanities and Social Sciences Communications, 10, 653. https://doi.org/10.1057/s41599-023-02200-0  

Yee, K., Whittington, K., Doggette, E., & Uttich, L. (2023). ChatGPT Assignments to Use in Your Classroom Today. UCF Created OER Works, (8). Retrieved from https://stars.library.ucf.edu/oer/8  

You've completed all the modules

We hope that you found these modules useful and engaging, and are better able to address AI chatbots in your teaching practice. Please continue to engage by joining or starting dialogues about AI within your communities. You might also take advantage of our peers across campus who are developing resources on this topic.

  • Institute for Human-Centered Artificial Intelligence
  • Accelerator for Learning
  • Office of Innovation and Technology , Graduate School of Education

We are continuing to develop more resources and learning experiences for the Teaching Commons on this and other topics. We'd love to get your feedback and are looking for collaborators. We invite you to join the Teaching Commons team .

assignment of artificial intelligence

Learning together with others can deepen the learning experience. We encourage you to organize your colleagues to complete these modules together. Consider how you might adapt, remix, or enhance these modules for your own needs. If you have any questions, contact us at [email protected] . This guide was c reated by Stanford Teaching Commons and is licensed under  Creative Commons BY-NC-SA 4.0 (attribution, non-commercial, share-alike).

Artificial Intelligence (AI) in Education

  • Background Information
  • AI and ethics
  • Academic integrity, syllabi statements, & AI

AI detection Tools

Use ai and document the work, move away from the five paragraph essay, in-class essays, collaborative activities & discussion, meaning-making activities, brain dump activities, explain the process, impromptu oral exams, more obscure reading selections, field observations, recommended readings, references for assignment ideas.

  • How to cite AI

CFI

Attribution

This page is based on Chatbots & Critical Pedagogy from  AI in the Classroom . 

AI Detection Tools are in development; however, they may not be reliable because they are just emerging. Faculty who choose to use a detection tool should use caution when interpreting results, because false positives are possible. TurnItIn's AI Detector is available through normal use of TurnItIn.

  • How do I create and grade a TurnItIn assignment?
  • What are Artificial Intelligence (AI), ChatGPT and AI Detection Tools?
  • TurnItIn's AI Detection
  • GenAI detection software not yet reliable enough (University of Pittsburgh)

The general practice of citation is that you cite anything that comes from somewhere else; anything that isn't your original thought, isn't common knowledge, and/or is a place where you pulled information from.

Where an assignment requires an AI source to be cited, you must reference all the content from tool that you include in your assignment. Failure to reference externally sourced, non-original work can result in scholastic dishonesty. References should provide clear and accurate information for each source and should identify where they have been used in your work.

  • AI archives The website extension saves your chatGPT or Bard conversations and creates a URL, which allows readers to reference the original conversation used by the author. This tool is particularly useful for creating citations.
  • Teaching Toolbox: Chat GPT Provides several ideas and suggestions that can foster responsible use of ChatGPT in assignments.

Chatbots can follow this format easily. Encourage your students' originality by moving away from this formulaic format.

  • Tip: If you want to stick with the five-paragraph essay, test out your prompt on an advanced chatbot like ChatGPT. Greene ( 2022 ) writes, "If it can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped... if you have come up with an assignment that can be satisfactorily completed by computer software, why bother assigning it to a human being?"
  • Sticking with essays? Warner ( 2022 ) suggests focusing on process rather than product. Scaffolding learning and allowing students to explain their thinking and make learning visible along the way are strategies that may help you confirm student originality: "I talk to the students, one-on-one about themselves, about their work. If we assume students want to learn - and I do - we should show our interest in their learning, rather than their performance."

In the short-term, you can have your students  write essays in class and on paper . 

  • For longer research papers, students will have access to chatbots outside of class.
  • Students may need to use online resources for their writing.
  • You won't be able to use the LMS feedback tools for annotation, rubric scoring, and grading.
  • Note: Some students may have accommodations to type their work rather than handwrite it. Make sure to follow student accommodations when assigning work

Use  collaborative activities and discussions to mitigate the use of chatbot responses in your class.

While students may generate ideas from a chatbot, they will need to discuss with one another whether they want to use the chatbot responses, if they fit the prompt, and if they are factually accurate.

  • These strategies can work for online courses with a few tweaks. For discussions, ask students to post a recording rather than text. While students may generate a response using ChatGPT, creating their video will require more interaction with the content than copy-pasting a text response would.

Engage your students in  meaning-making activities  to demonstrate their learning.

This could include: Skits*, Drawings and Sketches, Concept Mapping, Infographics*, Digital Storytelling*, or  Write* or revise Wikipedia articles  (Wiki Education). Other ideas from:

  • Let students choose a medium and activity  (“Digital Media Design Student Choice Board” by Torrey Trust is licensed under  CC BY NC SA 4.0 )
  • Fun formative assessment: 12 easy, no-tech ideas  (Ditch That Textbook)

* Note that a chatbot can provide an outline for these activities.

Brain dumps  are an ungraded recall strategy.

The practice involves pausing a lecture and asking students to write everything they can recall about a specific topic. Read more at:

  • Brain Dump: A small strategy with a big impact  (Retrieval Practice)

During or after writing, students explain their process or thinking.

Students could:

  • Use Comments in Word or Google Docs;
  • Create a video explaining their change history on a Google Doc;
  • Use Track Changes to show their revisions.

Consider using planned or impromptu oral exams.

You may consider including phrasing in your syllabus about conducting oral exams if you suspect plagiarism through the use of a chatbot.

When selecting readings, consider sourcing more obscure texts for your students to read.

Chatbots may have less information in their training data on obscure texts. As an example, the New York Times reports that, "Frederick Luis Aldama, the humanities chair at the University of Texas at Austin, said he planned to teach newer or more niche texts that ChatGPT might have less information about, such as William Shakespeare’s early sonnets instead of 'A Midsummer Night’s Dream'" (Huang, 2023). 

(Note that ChatGPT is currently trained on data through 2021. Some educators suggest using newer writings and research, but this strategy isn't foolproof since the training models for chatbots are updated frequently.)

Coordinate times to take your class to conduct field observations; students can note their observations and write a reflection about their experience.

  • A Teacher's Prompt Guide to ChatGPT Created by Centre for Education Statistics and Evaluation, New South Wales, Australia
  • Critical Questions about Technology We encourage you to approach chatbot tools with a critical lens before structuring course assignments with these tools. Some students may be unaware of these tools and what they can do, and others may only be thinking about how they can benefit from the tool.
  • ChatGPT and Assessment by Ean Henninger, UNM Office of Assessment
  • Teaching With and About AI By Lori Townsend, University Libraries

Aaronson, S. (2022, November 28).  My AI safety lecture for UT Effective Altruism .  Shtetl-Optimized: The blog of Scott Aaronson .

Bowman, E. (2023, January 9).  A college student created an app that can tell whether AI wrote an essay . NPR .

Caines, A. (2022, December 29).  ChatGPT and good intentions in higher ed.   Is a Liminal Space .

Caren, C. (2022, December 15).  AI writing: The challenge and opportunity in front of education now . Turnitin .

Chechitelli, A. (2023, January 13).  Sneak preview of Turnitin’s AI writing and ChatGPT detection capability . Turnitin .

Ditch That Textbook. (2022, December 17).  ChatGPT, chatbots and artificial intelligence in education .

Greene, P. (2022, December 11).  No, ChatGPT is not the end of high school English. But here’s the useful tool it offers teachers . Forbes .

Hick, D.H. (2022, December 15).  Today, I turned in the first plagiarist I’ve caught using A.I. software to write her work  [Facebook post]. Facebook .

Huang, K. (2023, January 16).  Alarmed by A.I. chatbots, universities start revamping how they teach . New York Times .

Kelley, K.J. (2023, January 19).  Teaching actual students writing in an AI world . Inside Higher Ed .

OpenAI. (2022, December).  ChatGPT FAQ .

Trust, T. (2023).  ChatGPT & education  [Google Slides]. College of Education, University of Massachusetts Amherst.

Warner, J. (2022, December 11).  ChatGPT can't kill anything worth preserving: If an algorithm is the death of high school English, maybe that's an okay thing .  The Biblioracle Recommends .

Watkins, R. (2022, December 18).  Update your course syllabus for chatGPT . Medium .

Wiggers, K. (2022, Decemer 10).  OpenAI’s attempts to watermark AI text hit limits . TechCrunch .

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assignment of artificial intelligence

Artificial Intelligence (AI)

Understand AI and how to work with it responsibly.

  • Expert help guides
  • Generative AI in your assignments

Can I use generative AI tools in my assignments?

Some subject coordinators may explicitly include information in your assessment guidance as to whether these kinds of tools may be used and how.  You must comply with the requirements of the assessment task – if you are unsure, check with your lecturer.

Tools such as AI chatbots can be helpful to explain concepts in different ways – this may help you to understand difficult concepts in your course.  Tools like ChatGPT can also potentially help you to think about an initial structure for an assignment: for example you might ask for section headings for a document based on your own notes, as a way to get started. 

In some cases, your lecturer may ask you to use ChatGPT or other generative AI tools as part of the assessment.  If this is the case, make sure that you understand how you are expected to use the tool and which parts of the work are expected to be your own original work.  If you are unsure, ask you tutor or lecturer for clarification. 

It is important to remember that when you submit an assignment or other assessment, you are taking responsibility for the content, and claiming it as your own work.  Whilst generative AI tools can be useful for helping you to understand a topic or structure your thinking, using them to write substantial parts of your work for you (where this is not explicitly required as part of the assessment) is academic misconduct and may have serious consequences for you.

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Center for Teaching Innovation

Ai in assignment design.

Using generative artificial intelligence (AI) can be both productive and limiting—it can help students to create and revise content, yet it also has the potential to undermine the process by which students create. When incorporated effectively into assignments, generative AI can be leveraged to stimulate students' ability to apply essential knowledge and develop critical thinking skills. 

As you explore the possible uses of generative AI in your course, note that establishing a general familiarity with generative AI and being mindful of accessibility and ethical concerns will be helpful. 

The following process may help you determine how to best incorporate generative AI into your course assignments.

Affirm What You Actually Want to Assess

As you decide how you might incorporate AI into your course, it’s important to revisit your current course assessment plan, most importantly your course learning outcomes —that is, the skills and knowledge you want students to learn and demonstrate by the end of your course. Once you have a clear idea of the specific skills/knowledge you want to assess, the following questions can help determine whether or not your current assignments are effective and assessing what you want them to assess:

  • Does my assignment call for the same type of thinking skills that are articulated in my class outcomes? For example, if my course learning outcome calls for students to analyze major themes in a work, is there risk of my final assignment prompting students to do more (e.g., synthesize multiple themes across multiple works) or to do less (e.g., merely identify a theme) than this outcome? If so, there may be a misalignment that can easily be addressed.
  • Does my assignment call for the same type of thinking skills that students have actually practiced in class? For example, if I am asking students to generate a research prospectus, have I given them adequate opportunity to develop—and receive feedback on—this skill in class?
  • Depending on your discipline, is there a need for an additional course outcome that honors what students now need to know about the use of generative AI in your course/field?

Explore When & How Generative AI Can Facilitate Student Learning

Once you have affirmed your learning outcomes and ensured that your assignments are properly aligned with those outcomes, think about if, when, and how it might make sense to incorporate generative AI. Is there a way to leverage generative AI to engage students in deeper learning, provide meaningful practice, or help scaffold your assignments?

Consider the usefulness of generative AI to serve as:

  • Have students analyze AI-generated texts to articulate what constitutes “good” (and not so good) responses to prompts.
  • Have students analyze AI-generated texts and engage in error analysis to develop more nuanced and discipline-specific writing skills.
  • Leverage the use of generative AI platforms to help students become more discerning. This can help students develop the critical thinking and information literacy skills required to effectively and responsibly use such platforms.
  • Have students revise AI-generated texts to develop critical thinking skills.
  • Have students engage with a generative AI platform as a tutor. 
  • Facilitate students’ responsible, self-guided use of generative AI to develop select discipline specific skills (e.g., coding in computer science courses)
  • Have students use generative AI to off-load repetitive tasks.
  • Have students use generative AI to conduct preliminary analysis of data sets to confirm broad takeaways and affirm that their more nuanced analysis is heading in the right direction.

Identify When Generative AI Cannot Facilitate Student Learning

It is often the case that students cannot—or should not—leverage generative AI to promote or demonstrate their own learning. To help ensure that your assignment design highlights students’ unique perspectives and underscores the importance of a (non-generative AI informed) discipline-specific process, consider how to emphasize metacognition, authentic application, thematic connection, or personal reflection.  

Even if another part of an assignment calls for the use of generative AI, the following strategies may supplement the uses of AI highlighted above and foster deep and meaningful learning:

  • Have students identify the successes and challenges they experienced throughout the completion of a project.
  • Have students set incremental goals throughout a project, highlighting next steps of a discipline-specific process, resources they used, and the steps about which they are enthusiastic/nervous.
  • Have students self-assess their work, identifying strengths and weaknesses of their product/effort.
  • Have students engage in problem-based learning projects, ideally in authentic settings (e.g., problems that focus on our local community, real-world challenges, real-world industries, etc.).
  • Have students present projects (and engage with) authentic audiences (e.g., real stakeholders, discipline-specific research partners, native-speaking language partners, etc.)
  • Have students connect select reading(s) to course experiences (e.g., labs, field experiences, class discussions). 
  • Leverage Canvas-based tools that promote student-to-student interactions (e.g., Hypothesis for social annotation or FeedbackFruits for peer review and feedback).
  • Have students provide a reflective rationale for choices made throughout the completion of a class project (e.g., an artist statement, response to a reflection prompt about personal relevance of source selections)
  • Have students connect course experiences/motivations to their own lived experiences.

Create Transparent Assignment Materials

Once you have thought about whether or not generative AI can be effectively incorporated into your assignments, it is important to create assignment materials that are transparent (Winkelmes, et al., 2019). Specifically, this means creating ways to communicate to students the task you are are requiring, along with its purpose and evaluative criteria:

  • Task. Students will benefit from having a clear and accessible set of directions for the project or assignment you are asking them to complete. 
  • Purpose. Students are often more motivated when they understand why a particular task is worth doing and what specific knowledge or skills they will develop by completing the assigned task.
  • Evaluative Criteria. Students benefit from having a clear sense of how their work will be evaluated and a full understanding of what good work looks like.

Communicate Your Expectations for Generative AI Use 

Regardless of the extent to which you incorporate the use of generative AI into your assignment design, it is essential to communicate your expectations to students. Sharing clear directions for assignments, communicating how students can be successful in your class, and promoting academic integrity serves both you and your students well. 

Example Assignment Policy Language for Generative AI Use

The following language on the use of generative AI may be helpful as you create directions for specific assignments. Please note that the following sample language does not reflect general, course-level perspectives on the use of generative AI tools. For sample course-level statements, see AI & Academic Integrity .

Prohibiting AI Use for a Specific Assignment

Allowing the use of generative ai for a specific assignment with attribution.

For full details on how to properly cite AI-generated work, please see the APA Style article, How to Cite ChatGPT . "

Encouraging the Use of Generative AI for a Specific Assignment with Attribution

For full details on how to properly cite AI- generated work, please see the APA Style article, How to Cite ChatGPT ."

Confer with Colleagues

There is almost always a benefit to discussing an assessment plan with colleagues, either within or beyond your department. Remember, too, that CTI offers consultations on any topic related to teaching and learning, and we are delighted to collaboratively review your course assessment plan. Visit our Consultations page to learn more, or contact us to set up a consultation.

2023 EducaUse Horizon Report | Teaching and Learning Edition. (2023, May 8). EDUCAUSE Library. https://library.educause.edu/resources/2023/5/2023-educause-horizon-report-teaching-and-learning-edition

Antoniak, M. (2023, June 22). Using large language models with care - AI2 blog. Medium. https://blog.allenai.org/using-large-language-models-with-care-eeb17b0aed27

Dinnar, S. M., Dede, C., Johnson, E., Straub, C. and Korjus, K. (2021), Artificial Intelligence and Technology in Teaching Negotiation. Negotiation Journal, 37: 65-82. https://doi.org/10.1111/nejo.12351

Jensen, T., Dede, C., Tsiwah, F., & Thompson, K. (2023, July 27). Who Does the Thinking: The Role of Generative AI in Higher Education. YouTube. International Association of Universities. Retrieved July 27, 2023.

OpenAI. (2023, February 16.). How should AI systems behave, and who should decide? https://openai.com/blog/how-should-ai-systems-behave

Winkelmes, M. A., Boye, A., & Tapp, S. (2019). Transparent design in higher education 

teaching and leadership: A guide to implementing the transparency framework institution-wide to improve learning and retention. Sterling, VA: Stylus Publishing .

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Model ai assignments 2020

Todd W. Neller, Stephen Keeley, Michael Guerzhoy , Wolfgang Hoenig, Jiaoyang Li, Sven Koenig, Ameet Soni, Krista Thomason, Lisa Zhang, Bibin Sebastian, Cinjon Resnick, Avital Oliver, Surya Bhupatiraju, Kumar Krishna Agrawal, James Allingham, Sejong Yoon, Jonathan Chen, Tom Larsen, Marion Neumann, Narges Norouzi Ryan Hausen, Matthew Evett Show 2 others Show less

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Research output : Chapter in Book/Report/Conference proceeding › Conference contribution

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

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T1 - Model ai assignments 2020

AU - Neller, Todd W.

AU - Keeley, Stephen

AU - Guerzhoy, Michael

AU - Hoenig, Wolfgang

AU - Li, Jiaoyang

AU - Koenig, Sven

AU - Soni, Ameet

AU - Thomason, Krista

AU - Zhang, Lisa

AU - Sebastian, Bibin

AU - Resnick, Cinjon

AU - Oliver, Avital

AU - Bhupatiraju, Surya

AU - Agrawal, Kumar Krishna

AU - Allingham, James

AU - Yoon, Sejong

AU - Chen, Jonathan

AU - Larsen, Tom

AU - Neumann, Marion

AU - Norouzi, Narges

AU - Hausen, Ryan

AU - Evett, Matthew

N1 - Publisher Copyright: © 2020, Association for the Advancement of Artificial Intelligence.

N2 - The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

AB - The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

UR - http://www.scopus.com/inward/record.url?scp=85106530092&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85106530092&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85106530092

T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

PB - AAAI press

T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020

Y2 - 7 February 2020 through 12 February 2020

Duke Learning Innovation and Lifetime Education

Artificial Intelligence and Assignment Design

Generative ai assignments.

There are both academic and practical reasons you may choose to incorporate generative AI assignments into your course. For example, you may believe that AI will be a skill needed in the students’ future careers in your field. Perhaps you see AI as a tool to help students deepen their understanding of and engagement with your content. You may see the introduction of AI into your classroom as a way to open a conversation about its ethical and academic implications. Integrating AI ironically allows instructors to think deeply about how to design assignments that cannot be easily generated by AI alone to deter plagiarism and cheating. This guide comes from the perspective that you are open to developing AI assignments.

Note, it is critical to develop AI policies for your course along with policies for specific AI assignments.

Considerations for Developing an AI Assignment

Alignment with your course goals.

In the development of AI assignments, the primary consideration is whether the use of AI will help your students achieve the learning goals of the course. Ask yourself, does this assignment help student gain skills and knowledge central to your course and field? Furthermore, consider whether the assignment is engaging enough to warrant incorporating AI. Are you asking students to go above and beyond the AI-generated content? An impactful assignment will challenge students to transform, expand upon, correct, or critique the information and content generated by AI or learning about themselves in relationship to AI. Educational pedagogy expert Derek Bruff gives further insight into how to think about AI assignments as they relate to course design in his blog post about AI and writing assignments .

Guidelines for Use

If you integrate AI into your assignments, be sure to discuss your expectations with your students. It is essential that they understand why you have decided to allow AI in the course and its role in their learning. Furthermore, students can be engaged in wider conversations about AI and its personal impact on their lives. The University of Calgary has developed a set of recommendations of how to start these conversations. One strategy is writing a code of conduct that emphasizes critical thinking and sets guardrails of proper use. You can provide a prewritten list of guidelines or work with the students from scratch by posing questions about AI and learning.

For example, the class may have guidelines such as:

  • We will only use AI to help our intellectual development, not replace it.
  • We will be transparent in our use of AI.
  • We will not submit AI generated text without attribution.
  • We will follow guidelines of when AI is appropriate to use.

Assignment Mechanics

Detailed instructions for an AI assignment will raise the chances for a successful learning experience. Students are not familiar with the processes of this novel type of intellectual work, and thinking through the different facets of the activity will help you to execute and evaluate the assignment confidently. Consider the following questions:

  • Are you allowing ample time to complete the assignment considering it is a new tool for students?
  • Is it better to do the assignment together in class or out of class?
  • Have you practiced using the technology together?
  • How should AI be cited? Are there specific steps for showing how the original AI text is changed?
  • What kind of prompts are allowed? What functions can AI be used for?
  • How will you provide feedback on their use of AI?

AI Literacy

Both you and your students should have a level playing field when it comes to understanding generative AI. You cannot count on students to understand the pitfalls and limitations of AI or even how to use the tools. There are existing resources on AI literacy developed specifically for students that can be a starting point. This library guide from the University of Arizona instructs students on AI, plus there is a companion guide for instructors as well.

Ethical Concerns

There are ethical issues to using AI beyond questions of plagiarism, copyright and academic integrity that should be considered. First, to minimize threats to the privacy of your students and yourself, personal information should not be shared. To dive deeper into privacy concerns, speak with students about the implications of AI services using our data to train their tools.

Second, students may not have equal access to the internet or sufficient funds for subscriptions to AI tools. Be sure to suggest several different AI tools and confirm that students are able to access at least one tool without paying for it. Not all students may take to generative AI equally and will not have the skills to architect effective prompts for your discipline or type of assignment. You can support them by modeling prompt generation or forming groups in class that can work together with AI.

Finally, for instructors who do allow AI for learning, there should be considerations for students who do not want to use it on ethical grounds. This could be solved by making AI assignments low-stakes or optional.

Types of Generative AI Assignments

Below are some general ideas of how to incorporate AI into your course. We encourage you to seek out examples from your discipline or related to the core skills of your course. Some resources worth exploring are ChatGPT asssignments to use in your classroom today (an open source book from the University of Central Florida) and a publication on coding and generative AI by an international group of computer science instructors. Instructors may also wish to leverage generative AI to help with routine tasks and lesson planning .

Brainstorming Ideas and Defining Concepts

Generative AI excels at summarizing content and explaining concepts. Warning to students, it is not necessarily 100% correct!

  • Users can ask AI to brainstorm research questions. “What are some examples of bank failures due to fractional reserve banking ?” Or, “What are some of the major events of the Cold War?”
  • Users can ask AI for clarification of concepts or terms they don’t understand.  “Explain fractional reserve banking in simple terms. ” Or, “What are the Federalist papers and why are they important?”
  • Instructors can ask for resources or ideas of how to teach students content.   “Provide an explanation of fractional reserve banking that discusses the pros and cons of its use .” Or, “What are some exercises to do in the classroom to teach the lifecycle of a butterfly?”

Writing Assistance

While it is possible to use generative AI to correct an entire essay, students can be instructed to prompt AI to provide limited feedback on specific aspects of their writing. Prompts could be limited in scope. For example, students can ask AI to:

  • Rate the clarity of an argument “How well did I explain X? ” Or, “Does this writing contain all of the standard sections of a case study ?”
  • Suggest alternatives “Rewrite the conclusion to better summarize the content.” Or, “What is another way to explain this idea?”
  • Comment on writing mechanics “Review the sentence structure in this essay.” Or, “Check this essay for passive voice.”
  • Provide advice for improvement “List the common grammar mistakes in the essay and provide an explanation of the errors.” Or, “How can I make this writing more upbeat?”

Collaborative Writing

One popular assignment helps instructors show why writing for yourself is important intellectual work. Students read an AI-generated essay and grade it with a rubric. As a class the students discuss its strengths and weaknesses. As a follow-up students can submit a revised essay. In one Yale course, the instructor told students to ask ChatGPT to write its own version of a writing prompt after the students had completed an assignment so they could compare their writing against it.

Another approach to collaboration is to ask AI to write a first draft of an assignment. Students then improve it by doing independent research to double-check the AI content and refining (or rejecting) the AI arguments. Students should record both the questions they asked and the generated text. Students can also be asked to write summaries describing what they learned from the AI search and what they changed. The SPACE framework is a powerful model for organizing these types of writing assignments; the article details the cycle of prompting AI, evaluating its output, and rewriting AI generated content.

Arguably, the greatest strength of generative AI tools may be their ability to write code. Computer scientists are especially concerned about assignments in entry-level programming classes. The way coding is taught may change over time due to AI, but there are short-term strategies that incorporate AI but demand student input. 

  • AI could be asked to generate small snippets of code that students integrate into a larger programming project. Students test, debug and refine the code.
  • After completing a coding assignment, students prompt AI to write a different implementation of the problem and analyze which is more efficient and why.
  • Instructors or students write faulty code and use ChatGPT to generate test cases and/or to fix the errors. 
  • Instructors take advantage of AI to generate more coding assignments and review questions for exams.

Two researchers from UC San Diego published the findings of a study about the attitudes of computer scientists to generative AI and possible directions for teaching coding in the future.

ChatGPT and other generative AI tools do not produce expository content only. They are also able to generate content in many creative genres, often with laughable results ( “Write a pop song in the style of Shakespeare” ) The breadth of the kinds of writing generative AI can mimic might provide the chance for humans to use generative AI to spark creativity in themselves. Student might ask AI to describe the life in the Middle Ages from the perspective of a midwife as inspiration to write a modern version, or as background information for writing in another genre. Generative AI can help instructors deliver content in new ways, for example introducing games into teaching. Instructors might ask AI to develop trivia questions for exam review or a game of 20 questions as an in-class activity.

Generative AI can be a coach for learning that supports both instructors and students. Students can easily get more information about what they don’t understand. AI can be an agent for adaptive learning allowing students to “pass” certain learning objectives and get additional practice on concepts and skills they haven’t mastered. By the same token, it can assist instructors who need to provide additional assistance to students and are pressed for time to find resources. Instructors can get ideas for teaching a skill or subject with activity descriptions and lesson plans. AI can generate practice problems or review questions for exam prep, which frees up time for instructors for other class prep.

There are also positive gains in equity when generative AI is used in a tutoring setting. A neurodiverse student may find conversations with a bot to be non-judgmental and less stressful when needing help. Non-native speakers can ask for word and concept definitions to level up their understanding of course content and context. The review and tutoring capabilities of AI can help all students to practice concepts and receive feedback on their progress.

Looking Ahead

Incorporating generative AI into education is not without peril. Students’ reliance on AI content could potentially lead to losing skills in academic writing. There is the risk that students might mistakenly believe that AI is inherently better at developing ideas and expressing information; leaving students uncomfortable adding their own voice to writing. Without training on how to check the validity of AI content and conduct independent research, students may miss out on how to evaluate sources and compare ideas.

Like it or not, at this moment it lands on educators to design courses and assignments to mitigate these risks and to have hard and timely conversations with students. It may feel like AI is encroaching on teaching and learning, but we should remember that there are many aspects of teaching that are as important as delivering content. These are skills that only human instructors can perform, such as

  • Providing real-time feedback on complex tasks
  • Grading or producing subjective or substantive work
  • Providing social or emotional support 
  • Teaching complex, interconnected concepts
  • Engaging in personal interactions

The future of teaching may increasingly focus on those skills that our students need to make sense of their world, engage with others productively and make connections across disciplines and concepts.

General Resources for AI Assignments

A Teacher’s Guide to Prompt ChatGPT , Andrew Herft

AI in the Classroom , UC Riverside

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AAAI

Association for the Advancement of Artificial Intelligence

Model AI Assignments 2020

February 1, 2023

Todd W. Neller

Gettysburg College

Stephen Keeley

Princeton University

Michael Guerzhoy

Princeton University, University of Toronto, Li Ka Shing Knowledge Institute

Wolfgang Hoenig

California Institute of Technology

Jiaoyang Li

University of Southern California

Sven Koenig

Swarthmore College

Krista Thomason

University of Toronto

Bibin Sebastian

Cinjon Resnick

New York University

Avital Oliver

Surya Bhupatiraju

Massachusetts Institute of Technology

Kumar Krishna Agrawal

Indian Institute of Technology, Kharagpur

James Allingham

University of Cambridge

Sejong Yoon

The College of New Jersey

Jonathan Chen

Washington University in St. Louis

Marion Neumann

Narges Norouzi

University of California, Santa Cruz

Ryan Hausen

Matthew Evett

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 34

Vol. 34 No. 09: Issue 9: EAAI-20 / AAAI Special Programs

EAAI Symposium: Model AI Assignments

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

10.1609/aaai.v34i09.7072

assignment of artificial intelligence

ISSN 2374-3468 (Online) ISSN 2159-5399 (Print) ISBN 978-1-57735-835-0 (10 issue set)

Published by AAAI Press, Palo Alto, California USA Copyright © 2020, Association for the Advancement of Artificial Intelligence All Rights Reserved

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  • AI Guidance & FAQs

Harvard supports responsible experimentation with generative AI tools, but there are important considerations to keep in mind when using these tools, including information security and data privacy, compliance, copyright, and academic integrity.   The Office of Undergraduate Education has compiled the following resources for instructors regarding appropriate use of generative AI in courses.

Generative AI Event Recordings

In August 2023, Amanda Claybaugh, Dean of Undergraduate Education, and Christopher Stubbs, Dean of Science, hosted informational sessions on the use of generative AI in courses. In each session, faculty presented examples of new assignments they have developed, as well as advice on how to “AI-proof” familiar assignments, and shared thoughts about how to guide students in using these technologies responsibly.

Generative AI in Your STEM Course - August 8, 2023

Generative AI in Your Writing Course - August 9, 2023

Policies for the Use of AI in Courses

We encourage all instructors to include a policy in course syllabi regarding the use and misuse of generative AI. Whether students in your course are forbidden from using ChatGPT or expected to explore its limits, a policy helps ensure that your expectations for appropriate interaction with generative AI tools are clear to students. Once you decide on a policy, make sure you articulate it clearly for your students, so that they know what is expected of them. More specifically, you should post your policy on your Canvas site .

Below is sample language you may adopt for your own policy. Feel free to modify it or create your own to suit the needs of your course.

A maximally restrictive draft policy:

A fully-encouraging draft policy:, mixed draft policy:.

Certain assignments in this course will permit or even encourage the use of generative artificial intelligence (GAI) tools such as ChatGPT. The default is that such use is disallowed unless otherwise stated. Any such use must be appropriately acknowledged and cited. It is each student’s responsibility to assess the validity and applicability of any GAI output that is submitted; you bear the final responsibility. Violations of this policy will be considered academic misconduct. We draw your attention to the fact that different classes at Harvard could implement different AI policies, and it is the student’s responsibility to conform to expectations for each course. 

Additional AI Resources

Ai pedagogy project.

Visit the  AI Pedagogy Project (AIPP) , developed by the metaLAB at Harvard, for an introductory guide to AI tools, an LLM Tutorial, additional AI resources, and curated assignments to use in your own classroom. The metaLAB has also published a quick start guide for  Getting Started with ChatGPT

Teaching and Artificial Intelligence

A  Canvas module , created by the Bok Center for Teaching and Learning, for instructors teaching in the age of AI that includes information on  creating syllabus statements ,  writing assignments , and  in-class assessments . The Bok Center also offers advice and consultations  for faculty seeking to respond to the challenges and opportunities posed by AI.

Teaching at the Faculty of Arts and Sciences

The Teaching at FAS website, a collaborative project between several college and university offices, offers a list of resources for Harvard faculty related to designing and teaching courses.

Frequently-Asked Questions about ChatGPT and Generative AI

What is chatgpt.

Generative artificial intelligence (GAI) tools such as Chat-GPT represent a significant advance in natural-language interaction with computers. On the basis of a ‘prompt’ a GAI system can produce surprisingly human-like responses including narrative passages and responses to technical questions. Moreover, an iterative exchange with the AI system can produce refined and tuned responses. This technology is evolving very rapidly. GAI systems have demonstrated the ability to pass the medical licensing exam, pass the bar exam, to generate art and music, answer graduate level problems from physics courses. These GAI systems are far from perfect, and some of the material they provide as factual are incorrect. GAI technology is a disruptive and rapidly changing new technology that will impact many aspects of our lives. 

Weaknesses of many GAIs at present include their inability to perform basic arithmetic calculations, and the propensity for ‘hallucinations’. Also, the GAI responses will reflect the biases and inaccuracies that are contained in the training data. It’s important to realize that there is an intentional ‘random’ element in the responses for most GAI systems. The same input does not always produce the same output. Also, the provenance of information that is used in responses does not flow through to the output, and this limits our ability to perform validation. But GAI capabilities are changing rapidly and we should anticipate ongoing refinements and progress. We currently don’t think twice about use spell-checking and grammar-checking tools in word processors, picking a suggested next-word when composing a text message on a phone, or using electronic calculators and spreadsheets. It will be interesting to observe whether GAI tools will become similarly integrated into everyday workflow. 

How does this affect our teaching?

Generative AI systems can produce responses to homework problem sets, to essay assignments, and to take-home exam questions. We should assume that all our students are proficient with these tools and should adjust our expectations accordingly. While it’s true that our students could previously draw upon various resources to avoid doing assignments themselves, the ease-of-use, free access, and high performance of GAI systems have raised this to a new level. Our challenge is to incentivize the level of engagement that leads to a deeper understanding and the development of the habits of mind we hope to instill in our students. 

A good first step is to feed representative assignments from your upcoming courses into a GAI tool such as ChatGPT and take a look at what it produces. Then assume that if given the opportunity, many of the students in your course are likely to do the same thing. Based on this insight, decide how to best adapt to, adjust to, and as appropriate incorporate GAI into your instructional plans. Then decide on and disseminate a course-by-course policy on student use of GAI tools. For more guidance about generative AI in teaching, visit the  Bok Center’s AI resources .

As this situation evolves, we need to learn how to best use these tools to enhance learning. We also need to teach our students how to use these tools in an ethical and responsible manner. 

How can I get a ChatGPT account?

Go to chat.OpenAi.com and register for an account. It’s free. You can find many quick-start guides online. 

Be sure to review Harvard’s guidelines for use of these tools at  https://huit.harvard.edu/ai .

Is there a technology that can detect unauthorized use of ChatGPT?

There are a variety of tools that claim various degrees of success in finding instances when GAI was used, but this is something of an arms race. It would be inadvisable to count on automated methods for GAI detection. FAS does not plan to provide/license such a tool for use in courses.

Is there a technology that can block students from accessing the internet so that they can use their laptops for in-class exams?

Is it appropriate to enter student work into chatgpt to generate feedback, or for students to enter their work into chatgpt.

No confidential information can be loaded into GAI systems, since there is no expectation of privacy or confidentiality. Faculty must get documented permission from students before putting original student content into any generative AI tool, and students should be made aware of the risks of entering their original work into such tools. 

ChatGPT’s terms of service allow the company to access any information fed into it.

What tools do we and our students have access to?

Harvard HUIT has compiled a list of available tools at https://huit.harvard.edu/ai/tool .

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Model AI Assignments

2024 call for assignments, project archive, important dates:.

  • September 10, 2023: : Model AI Assignment 200-word abstracts (via EasyChair) and assignment submissions (via email) due
  • December 9, 2023: Notification of acceptance or rejection
  • February 24-25, 2024: Symposium dates

What is the Model AI Assignments Session?

The Model AI Assignments Session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community.

One must learn by doing the thing; for though you think you know it, you have no certainty, until you try. - Sophocles

Recognizing that assignments form the core of student learning experience, we invite AI educators to submit draft assignment materials that exemplify an approach to teaching AI topics at all levels.

Submission Ideas

Consider these challenges in assignment design:

  • Intro AI audience: Submit your favorite assignment that grounds one of the core AI concepts at the introductory level (e.g. search, constraint satisfaction, knowledge representation and reasoning, planning, probabilistic reasoning, machine learning, robotics, machine vision, etc.).
  • K-12/CS1/CS2 audience: Some AI assignment experiences are designed to communicate the techniques, potentials, and challenges of the discipline.  Submit an assignment that you believe will be most likely attract the next generation of AI practitioners.
  • Emerging topics: When a new algorithm has high impact in a research area, there is a need to introduce the algorithm not only to students, but to all AI researchers as well.  The creation of initial high- quality assignments to teach such groundbreaking techniques can accelerate research advancements and keep AI material fresh at all levels.  Submit an assignment which introduces an recent new algorithm or emerging subfield.

Whether sharing the best of your time-tested assignment designs, or offering a timely new creation, please consider how your creative assignment ideas can attract and prepare the next generation of AI researchers, or accelerate the advancement of the current generation.

The Model AI Assignment Session is a part of EAAI-24: The Fourteenth Symposium on Educational Advances in Artificial Intelligence.  This will be held February 24-25, 2024 in conjunction with AAAI-24 in Vancouver, British Columbia, Canada.

Registration: To register for EAAI, participants can register through the AAAI conference website and specify EAAI registration.

Student and Faculty Scholarships: Thanks to the generosity of the National Science Foundation, we are able to offer a limited number of scholarships to provide partial support for the costs involved in attending EAAI. Please see http://eaai.stanford.edu/scholar.html for more details.

EAAI-18 Organizing Committee: Sven Koenig (co-chair), University of Southern California ( [email protected] ) Eric Eaton (co-chair), University of Pennsylvania ( [email protected] ) Zachary Dodds, Harvey Mudd College ( [email protected] ) Todd Neller, Gettysburg College ( [email protected] ) Matthew Taylor, Washington State University ( [email protected] ) Sheila Tejada, University of Southern California ([email protected]

Model Assignment Considerations

As with SIGCSE's Nifty Assignments , EAAI Model AI Assignments should be:

  • Adoptable - Provide materials to make the assignment easy for other instructors to adopt.  Materials might include handouts in common formats (e.g. HTML, PDF), starter source code, data files, suggestions for use, etc. 
  • Engaging - Model assignments often have a playful "fun factor" or impressive outcome.  The applications spark interest in the topic, lead to deeper understanding, and are accomplishments likely to be shared with others.
  • Flexible/Scalable - Language/platform independence, while not required, would allow more widespread use over time.  Suggestions of possible follow-on projects, further readings, or other open ends invite continued learning.  Advice for variations in assignment design can help other instructors create unique (i.e. not easily plagiarized) assignment experiences for their students.

We especially encourage assignments that teach or demonstrate how AI can benefit humanity.

Submissions

Model Assignment submissions must be made in two parts:

  • Assignment submission (via e-mail) at the full paper submission deadline
  • 200-word abstract submission (via EAAI EasyChair submission site ) also at the full paper submission deadline

Assignment submission directions: Create a directory named with your last name, a hyphen, and the name of the assignment (e.g. Dodds-SetOpenCV).  Place all of your materials in this directory, and create an index.html file.   (Our goal is to create an archive of assignments, rather than link to resources elsewhere.  We will gladly update materials in our archive as authors direct.)  In this index.html file, create a table like the one below filled in with brief information about your assignment (replace the italicized text with your own):

Then add links to the assignment materials in your directory (e.g.  handouts in common formats (e.g. HTML, PDF), starter source code, data files, suggestions for use, etc.).  These need not be polished for submission; a draft is sufficient. Assignments should be anonymous for blind review. (Your directory will be renamed with a number before review.)

Finally, zip the directory (e.g. Dodds-SetOpenCV.zip), and email it to Todd Neller ( [email protected] ) with the subject line "Model Submission: " followed by the filename. (Please add "Model" to the subject line for all Model Assignments Session email.)

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ARTIFICIAL INTELLIGENCE AT NORTHWESTERN

Assignments, generative artificial intelligence and assignments.

Once you've chosen the policy framework for students' use of generative artificial intelligence (GAI) in your course, you will need to extend that guidance to your assignments and assessments.

Generative AI framework

(Framework inspired by Forbes, M. & Brandauer J. "What’s my stance on  genAI  in this class?"  Gettysburg College Johnson Center for Teaching and Learning. Retrieved 8/20/2023 from  https://genai.sites.gettysburg.edu/positions-and-policies )

If you choose to "close" a particular assignment to the use of GAI, you may want to articulate your rationale. Closing the assignment may mean that you will need to redesign it. You might consider using guidelines for the assignment that ask students to do what text-generating Large Language Model (LLM) tools such as ChatGPT, Bard, or Claude do not do well. For example, you could:

  • Make the assignment very current , as some LLMs have knowledge cutoff dates of more than a year ago. ​
  • Incorporate  hyperlocal  context, which may have intrinsic appeal to students and may encourage critical understanding about the LLM's capacity to yield worthwhile outputs. ​
  • Add clear source and  citation requirements if they differ from general course expectations. ​
  • Add specific elements to a rubric  that assess critical thinking.

You might also follow the guidelines of the Universal Design for Learning and focus on multiple means of expression. Students could:

  • Create video or audio responses to the assignment, rather than text
  • Annotate in a text using Perusall or Hypothesis , which are integrated with Canvas. 

While in-class written work and high-stakes assessments can effectively "close" assessments to GAI, keep in mind that these practices may put  students with accommodations through AccessibleNU at a disadvantage and can exacerbate student stress. 

Conditional or Open: Permitting or Requiring GAI Use

If your course is open or if you choose conditional use of GAI for your course, consider the following elements when permitting or requiring students to use GAI on assignments:

  • Students should only be required to use what is available and free
  • Share your reasoning for the use of GAI in the assignment and its value to students
  • Ask students to use only data that are not private or personal (See Syllabus statement for instructors who engage students in using generative AI systems/software )
  • Add a warm-up exercise to familiarize students with the tools you have chosen
  • Be specific about how you would like the students to use the GAI and explain why: is it a starting point or idea generator? A debate partner? An editor for student-authored work?
  • Incorporate reflective opportunities to inspire metacognition
  • Consider the principles of the Universal Design for Learning and allow multiple means of expression (presentations, video essays, etc.)
  • Familiarize your students with the Northwestern University Library citation guidelines for GAI

Some ideas for having students use LLMs as a starting point for an assignment include:

  • Start with an  i ntroductory exercise  that provides an  ethical use case of ChatGPT ​
  • Brainstorm by asking an LLM questions about the material or subject (theories, frameworks, problems, etc.)
  • Ask an LLM to brainstorm ideas for a project​, such as a new business idea
  • Ask an LLM for feedback on their work
  • Ask an LLM to summarize a source they have consulted
  • Ask an LLM to analyze data they have gathered
  • Prompt the LLM to take one side of a debate ​
  • Prompt the LLM to write a sonnet on a particular topic and compare it with an existing sonnet ​
  • Prompt the LLM to summarize a historical event, person, or period and have students discuss, correct, interrogate for accuracy and  credibility ​

As part of the assignment, students could be asked to explain their use of GAI:   ​

  • Include a reflective paragraph  on their  LLM usage that details how they used it, what it provided and why it was or was not beneficial to their final product ​
  • Include a copy of all prompts and text from  the LLM  as an  appendix
  • Identify issues of bias, relevance, and accuracy that they encounter while using an LLM
  • Post using Discussions in Canvas to share work with the LLM while it is in progress

Example: Ask ChatGPT to write an essay on your topic

"You have been researching a particular topic for your final presentation. I'd like you to ask ChatGPT 3.5 to write a 500-word essay on the historical importance of your topic. Copy that essay into Word, along with the prompt you gave it. Then turn on Track Changes in Word and edit the essay: correct any errors, verify any facts that ChatGPT cited by putting a comment on the fact and showing at least one other source for it, improve the writing to make the essay clearer and more interesting. Then write a paragraph or two titled "Feedback" that explains your overall assessment of the ChatGPT essay and give it a grade. Submit your Word document to Canvas."

Rationale: This assignment falls part way through a course, after students have developed expertise on a topic. By engaging with ChatGPT, students will:

  • Express their expertise by corroborating or debunking the items in the essay
  • Use historical analytical skills by verifying facts and checking other sources
  • Use editing skills to improve writing
  • Put themselves in an evaluative mode and explain their thinking. 

Consider the following examples of assignments that have been adapted to make use of GAI in ways that will advance learning:

Example 1: A communication and marketing plan

Part of the original assignment asked students to develop a communication and marketing plan, which took about three weeks.

The revised assignment instructed students to ask ChatGPT to draft multiple communication and marketing plans. Next, the students are required to analyze the results; identify, with justification, the best elements of the various plans; and adapt these into a single plan.

Rationale: This assignment may shorten the amount of time devoted to the nuts and bolts of the assignment - developing the plan - allowing the students more time to evaluate, analyze, and synthesize.

Example 2: A lab report

Part of the original assignment asked students to gather data and write a lab report detailing the purpose, methods, and findings of their experiment.

In the revised assignment, students were given an editable, ChatGPT-generated lab report as an example of "C" quality work.  In addition, they were familiarized with the rubric for evaluation. Students were asked to update the lab report with their own results, edit its analysis, and try to improve it from "C" to "A" quality work.

Rationale: This assignment will not shorten the amount of time devoted to the laboratory work, but it may deepen students' analysis and editing skills. 

Video Examples

  • Ignacio Cruz, Assistant Professor, Communication Studies, "Classroom Activity: AI-Enabled Hiring."
  • Ken Alder, Professor, History, "Assignment: Using ChatGPT for Research Projects."

See the Northwestern University Writing Program AI Resources site for an extensive list of ways to incorporate generative AI into writing assignments.

assignment of artificial intelligence

Artificial Intelligence (AI), Features, Type, Application, Advantage and Disadvantages

Artificial Intelligence (AI) refers to the development of computer systems capable of performing tasks that typically require human intelligence.

Artificial-Intelligence

Table of Contents

In the age of information and technology, one term has emerged as a driving force behind innovation and transformation across diverse sectors – Artificial Intelligence (AI). AI, often referred to as the cornerstone of the Fourth Industrial Revolution, is reshaping the landscape of industries, governance, and society as a whole. As aspirants for the prestigious UPSC exams, it is essential to comprehend the profound impact of AI on our world and its implications for the future.

What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) refers to the development of computer systems capable of performing tasks that typically require human intelligence. Examples of AI include:

  • Natural Language Processing (NLP) : AI-powered chatbots like Siri and Google Assistant understand and respond to human speech.
  • Machine Learning Algorithms: Netflix’s recommendation system uses AI to suggest personalized content based on user preferences.
  • Computer Vision: AI can analyze images and videos, enabling self-driving cars to recognize traffic signs and faces in photos.
  • Medical Diagnosis: AI assists doctors in diagnosing diseases by analyzing medical images and patient data.
  • Game Playing: AI systems like Deep Blue and AlphaGo have beaten human champions in chess and Go.

AI continues to advance, impacting diverse fields from healthcare and finance to entertainment and autonomous vehicles.

Father of Artificial Intelligence (AI)

John McCarthy, an American computer scientist, is often regarded as one of the founding figures of Artificial Intelligence (AI). He is famously credited with coining the term “artificial intelligence” and played a significant role in establishing the field. Alongside other luminaries like Alan Turing, Marvin Minsky, Allen Newell, and Herbert A. Simon, McCarthy contributed to the foundational concepts and early developments that paved the way for the field of AI. His work and vision have left an indelible mark on the world of computer science, making him a pivotal figure in the history of AI.

Artificial intelligence and machine learning

Open artificial intelligence.

“Open Artificial Intelligence,” often known as “OpenAI,” is a prominent organization dedicated to advancing artificial intelligence for the benefit of humanity. OpenAI’s core mission is to promote the development of AI technologies that are transparent, safe, and accessible to all. They are known for creating state-of-the-art AI models like GPT-3, which have wide-ranging applications in natural language understanding, text generation, and more. OpenAI emphasizes collaboration, sharing research, and promoting responsible AI practices. By fostering openness and cooperation in the AI community, OpenAI aims to drive innovation while ensuring ethical, secure, and equitable AI development and deployment for the betterment of society.

Generative Artificial Intelligence

Generative Artificial Intelligence, often referred to as Generative AI, is a subset of artificial intelligence focused on creating systems capable of generating content autonomously. These systems use techniques such as neural networks, deep learning, and reinforcement learning to produce text, images, music, or other data types. Generative AI has led to significant advancements in creative fields, content generation, and automation. Examples include text generation models like GPT-3, which can compose articles, answer questions, and even generate poetry, and deep generative models like GANs (Generative Adversarial Networks), which produce realistic images and videos. Generative AI is revolutionizing content creation and creative industries across the board.

Types of Artificial Intelligence (AI)

Narrow or weak ai (ani).

Also known as narrow AI or weak AI, this type of AI is designed for a specific task or a limited range of tasks. ANI systems excel in their designated area but lack general intelligence. Examples include virtual personal assistants (like Siri or Alexa), recommendation algorithms, and chatbots.

General or Strong AI (AGI)

General AI, also called strong AI, aims to replicate human-level intelligence, with the ability to understand and perform a wide range of tasks, adapt to new situations, and learn new skills. Achieving AGI is an ongoing research challenge and has not yet been realized.

Artificial Superintelligence (ASI)

This hypothetical form of AI surpasses human intelligence in every aspect, including creativity, problem-solving, and emotional intelligence. ASI represents an advanced stage of AI development and remains a topic of debate and speculation.

Machine Learning (ML)

Machine learning is a subset of AI that focuses on developing algorithms and models that can learn from data. It includes supervised learning, unsupervised learning, and reinforcement learning.

Deep Learning

Deep learning is a subfield of machine learning that uses neural networks with multiple layers (deep neural networks) to analyze and process data. It has been particularly successful in tasks like image and speech recognition.

Natural Language Processing (NLP)

NLP is a branch of AI that deals with the interaction between computers and human language. It enables machines to understand, interpret, and generate human language, making it essential for chatbots, language translation, and sentiment analysis.

Computer Vision

Computer vision AI systems can analyze and interpret visual information from the world, such as images and videos. They are used in applications like facial recognition, object detection, and autonomous vehicles.

Reinforcement Learning

Reinforcement learning is a type of machine learning where an AI agent learns to make decisions by interacting with an environment and receiving rewards or penalties based on its actions. It is commonly used in robotics and game playing.

Expert Systems

Expert systems are AI programs that emulate the decision-making abilities of a human expert in a particular domain. They use knowledge bases and inference engines to provide solutions and recommendations.

Autonomous AI

These AI systems operate independently and make decisions without human intervention. Autonomous vehicles, drones, and some industrial robots are examples of autonomous AI.

Applications of artificial intelligence

Artificial Intelligence (AI) has witnessed a proliferation of applications across numerous industries, leveraging its data-driven capabilities to enhance efficiency, productivity, and decision-making. Here are some key domains where AI is making a significant impact:

AI is transforming healthcare by aiding in Medical Diagnosis, using machine learning to analyze patient data, and medical images for more accurate diagnoses. It also plays a crucial role in Drug Discovery, by sifting through vast datasets to identify potential drug candidates. Furthermore, AI enables Personalized Treatment Plans, tailoring healthcare interventions based on an individual’s genetic profile and medical history.

The financial industry benefits from AI in multiple ways. Algorithmic Trading employs AI algorithms to analyze market data and execute high-frequency trades, while AI-driven Fraud Detection systems identify anomalous transactions and protect against financial scams. In addition, AI assists in Credit Scoring, assessing an individual’s creditworthiness by analyzing their financial history and behavior.

Transportation

AI powers the development of Self-Driving Cars, using sensors and machine learning algorithms to navigate autonomously, promising safer and more efficient transportation. AI is also deployed in Drones, which can be used for tasks like surveillance, delivery, and inspection.

In the retail sector, AI-driven Recommendation Systems enhance customer experiences by suggesting products based on previous interactions. It optimizes Inventory Management, helping retailers maintain optimal stock levels and reduce waste. Furthermore, AI Chatbots offer automated customer support and handle inquiries efficiently.

NLP is a critical aspect of AI, facilitating Language Translation by enabling machines to interpret and translate text and speech between languages. AI also performs Sentiment Analysis, gauging public opinion and sentiment through social media and news data. Additionally, NLP powers Virtual Assistants like Siri and Alexa, making it easier to perform tasks through voice commands.

Manufacturing

AI plays a vital role in manufacturing through Predictive Maintenance, where it predicts equipment failures by analyzing sensor data, reducing downtime and maintenance costs. AI also ensures product quality through Quality Control, employing image analysis to identify and rectify defects in real-time.

Entertainment

In the entertainment industry, AI enhances gaming experiences with intelligent non-player characters and realistic graphics. It also contributes to Content Generation, creating music, art, and even scripts for movies and TV shows.

Agriculture

AI revolutionizes agriculture with Precision Farming, utilizing data from sensors and satellites to optimize crop planting, irrigation, and resource allocation for higher yields. Additionally, AI helps in Pest and Disease Management by identifying and mitigating agricultural pests and diseases more effectively.

Cybersecurity

AI aids in Threat Detection, identifying and responding to cybersecurity threats in real-time by analyzing network traffic and patterns. It is also instrumental in Anomaly Detection, detecting unusual behaviors or deviations from established norms that may indicate security breaches.

AI optimizes the energy sector with improved Grid Management, ensuring efficient distribution and stability in smart grids. Furthermore, AI predicts Energy Consumption Patterns, aiding in energy production and resource allocation for more sustainable energy practices.

Future of Artificial Intelligence (AI)

The future of Artificial Intelligence (AI) holds immense promise and presents both opportunities and challenges. As AI technologies continue to advance, we can expect greater integration into various aspects of our lives. This includes more sophisticated virtual assistants, improved healthcare diagnostics, autonomous vehicles becoming mainstream, and AI-driven solutions in areas like climate modeling and drug discovery. Ethical considerations and responsible AI development will gain even more importance, with a focus on fairness, transparency, and data privacy. Striking a balance between harnessing AI’s potential for innovation while addressing its ethical and societal implications will be a key aspect of shaping the AI landscape in the future.

Artificial Intelligence (AI) in Indian Context

In the context of India, the application of Artificial Intelligence (AI) holds immense potential to address specific challenges and bolster various developmental initiatives. AI can significantly complement the Digital India mission by enabling advanced data analysis, enhancing the targeted delivery of services, and strengthening security infrastructure, especially at borders.

Moreover, AI-powered weather forecasting models can aid in proactive disaster management, reducing the impact of calamities such as floods and droughts. The integration of AI in governance, healthcare, and remote areas can bridge critical gaps in service delivery.

Despite these opportunities, India faces challenges related to private sector dominance in AI, the absence of effective public-private funding models, an outdated education system, and the ongoing debate about resource allocation between poverty alleviation and technological advancement. To fully leverage AI’s potential, India must address these challenges and foster an enabling environment for AI research and development.

Advantages of Artificial Intelligence (AI)

  • Enhanced Accuracy: AI algorithms can analyze vast amounts of data with precision, reducing errors and improving accuracy in various applications, such as diagnostics, predictions, and decision-making.
  • Improved Decision-Making: AI provides data-driven insights and analysis, assisting in informed decision-making by identifying patterns, trends, and potential risks that may not be easily identifiable to humans.0
  • Innovation and Discovery: AI fosters innovation by enabling new discoveries, uncovering hidden insights, and pushing the boundaries of what is possible in various fields, including healthcare, science, and technology.
  • Increased Productivity: AI tools and systems can augment human capabilities, leading to increased productivity and output across various industries and sectors.
  • Continuous Learning and Adaptability: AI systems can learn from new data and experiences, continually improving performance, adapting to changes, and staying up-to-date with evolving trends and patterns.
  • Exploration and Space Research: AI plays a crucial role in space exploration, enabling autonomous spacecraft, robotic exploration, and data analysis in remote and hazardous environments.

Disadvantages of Artificial Intelligence (AI)

  • Job Displacement: AI automation may lead to the displacement of certain jobs as machines and algorithms can perform tasks that were previously done by humans. This can result in unemployment and require re-skilling or retraining of the workforce.
  • Ethical Concerns: AI raises ethical concerns such as the potential for bias in algorithms, invasion of privacy, and the ethical implications of autonomous decision-making systems.
  • Reliance on Data Availability and Quality: AI systems heavily rely on data availability and quality. Biased or incomplete data can lead to inaccurate results or reinforce existing biases in decision-making.
  • Security Risks: AI systems can be vulnerable to cyber attacks and exploitation. Malicious actors can manipulate AI algorithms or use AI-powered tools for nefarious purposes, posing security risks.
  • Overreliance: Blindly relying on AI without proper human oversight or critical evaluation can lead to errors or incorrect decisions, particularly if the AI system encounters unfamiliar or unexpected situations.
  • Lack of Transparency: Some AI models, such as deep learning neural networks, can be difficult to interpret, making it challenging to understand the reasoning behind their decisions or predictions (referred to as the “black box” problem).
  • Initial Investment and Maintenance Costs: Implementing AI systems often requires significant upfront investment in infrastructure, data collection, and model development. Additionally, maintaining and updating AI systems can be costly.

Artificial Intelligence (AI) UPSC

Artificial Intelligence (AI) stands as a transformative force, reshaping industries, governance, and our daily lives. It encompasses diverse applications, from healthcare and finance to transportation and entertainment, promising efficiency and innovation. India, in particular, can harness AI’s potential to bolster developmental initiatives, such as Digital India and disaster management. However, challenges persist, including private sector dominance, inadequate funding models, and an outdated education system. The ethical dimensions of AI also demand vigilant consideration. Nonetheless, as AI continues to evolve, responsible adoption and ethical oversight can unlock a future where AI complements human endeavors, offering both solutions and opportunities across the spectrum of human endeavors.

Featured Image Credit : ClaudeAI.uk ( https://claudeai.uk/)

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Artificial Intelligence (AI) FAQs

Who is the father of artificial intelligence.

John McCarthy, an American computer scientist, is often regarded as one of the founding figures of Artificial Intelligence (AI).

What are the different types of AI?

There are primarily three types of AI: Narrow or Weak AI (ANI): Specialized AI designed for specific tasks. General or Strong AI (AGI): AI with human-like general intelligence. Artificial Superintelligence (ASI): Hypothetical AI surpassing human intelligence in all aspects.

What is the role of Machine Learning in AI?

Machine Learning (ML) is a subset of AI focused on developing algorithms that enable machines to learn from data.

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AI-Resistant Assignments

Last Updated: Winter/Spring 2023

  • Assignments that emphasize the writing process, rather than just the final product, will discourage students from using AI tools to do their work for them.
  • Process-oriented assignments also tend to be more engaging and impactful for students.
  • Framing your writing assignments as extensions of the class discourse challenges students to respond to the course content with new ideas of their own (something AI generally can’t accomplish).
  • Making revision integral to the assignment helps students critically examine and improve their writing process, while making it harder to take AI-assisted shortcuts.
  • Reflective and metacognitive writing assignments make students’ learning visible to them and to you; it also holds students accountable for the intellectual work of your course.

See assignment prompts that incorporate one or more of these principles.

At the time of this writing (winter 2023), the AI writing tool ChatGPT has recently launched, with several competitors soon to follow, and it’s clear that artificial intelligence will have a massive impact on the way we write, both inside and outside academia, for the foreseeable future.  As AI continues to evolve, so will our teaching practices, but one immediate concern for many instructors is the possibility that students may use ChatGPT and similar text-generation tools to avoid the intellectual work prescribed by many of our writing assignments.  

While the current generation of AI tools can’t produce particularly insightful or effective academic writing, that doesn’t mean students won’t try to substitute AI-written work for their own.  Furthermore, savvy users have already found more subtle ways to use ChatGPT to make their work as writers easier, such as using it to produce revisable first drafts or to shortcut the research process by identifying and summarizing a variety of sources on a given topic.  Whether these practices constitute academic misconduct or not is largely up to individual instructors to decide, but if you would personally consider them inappropriate for your courses, it’s worth considering how you might design new writing assignments or reconfigure old ones to make them less susceptible to digital assistance.

Fortunately, many of the existing best practices for designing writing assignments can also make them more difficult to complete with AI tools.  More importantly, though, they can also discourage students from turning to AI in the first place by placing greater value on the writing process, rather than just its product.  This helps to make the learning process more transparent and makes assignments more engaging, accessible, and impactful for students who complete them faithfully.

Making Writing an Extension of the Class Discourse

The best writing assignments will often ask students to engage not just the subject matter or source material of the course, but the discourse around that material that develops within the class itself.  A prompt, for example, might explicitly require students to respond to ideas that have come up in class discussion, or to apply concepts or themes that you’ve examined in class to a new text or subject.  However you frame it, the key to this approach is asking students to contribute something new to the discussion that explicitly builds on ideas they’ve encountered in your course.  In other words, ask students to make their writing an extension of the class itself, rather than an exercise that just happens to address the same basic material. This distinction can be subtle, but here are a few sample prompts that use this approach effectively .

This might not seem like a revelatory practice–after all, don’t all essay assignments require students to engage core ideas from the course?  But there’s an essential difference between simply engaging ideas from a course and responding to them effectively in conversation.  Engagement simply requires a student to understand the material and react to it.  Responding requires them to have a working understanding of the discourse around the material — who has said what, where previous speakers have agreed, disagreed, complemented and contradicted each other, and, most crucially, what remains to be said.  

As scholars, we all understand this implicitly; we review and cite other scholars because we want readers to see exactly where and how we’ve built upon the ideas that have come before, and we craft our arguments in ways that actively invite future scholars to respond to them.  Students, however, especially students who are new to college-level writing, tend not to think of their writing this way until they’re explicitly taught to do so.  High school classes and high-stakes standardized exams generally train students to see writing as a series of hurdles which they must complete for the sole purpose of demonstrating that they know the course material and can explain it in their own words.  Consequently, students often approach their writing exactly the way an AI would: by seeking to remix other people’s ideas in a way that fulfills the prompt, whether or not they say anything particularly new or interesting along the way.

Framing assignments as a way to build on the class discourse requires students to take a more active role through their writing, and in the course itself.  Once they come to understand that their writing will be an extension of the conversation taking place in the classroom, they start to realize that the more they participate in (or at least actively pay attention to) that conversation, the easier it becomes to engage it in writing.  They also become more invested in their writing, as they start to see the essay as a distillation of ideas that grew out of real interactions with real people, not a detached set of musings composed in isolation and shouted into the void (or the cloud).  

The writing produced this way is often messy, over-energetic, unfocused, underdeveloped, or otherwise heavily in need of revision. But it also tends to have a life and a voice that’s distinctive to the course, the term, the class, and the student all at once, and AI tools cannot match this distinctiveness. An AI can learn in the sense that it can gradually produce more refined output, but it can’t understand the ideas it synthesizes well enough to add anything distinctive and relevant to them. It can’t think , and that’s exactly what a good writing assignment will require students to do, above all else. Thus, any assignment that requires students to think — and to articulate their thinking clearly — in response to the ideas they encounter will resist AI’s intervention.

Creating Space for Revision

Most instructors encourage their students to make at least some effort to revise their writing.  We may implore them to start early, to consult the writing center, to read their essays out loud or to a friend, to form peer review groups, and so on. But it’s another thing entirely to make revision an integral and visible part of an assignment. Doing this not only places direct value on revision, but it also allows students to see and understand how revision works and why it’s so crucial to good writing and good thinking.

It’s important at this point to articulate a distinction that might seem obvious to any experienced writer, but is often revelatory to students: revision is not the same as editing. Editing (in this context, anyway) means examining and improving the presentation of one’s ideas–the grammar, the phrasing, the formatting, etc. Revision, on the other hand, means examining and improving the ideas themselves–fundamentally re-envisioning one’s conclusions and the tapestry of sources, responses, counter-responses, and epiphanies that lead to them. Most students don’t fully understand this distinction or, if they do, are not sure how to apply it to their own writing. They need to be taught how to revise, and this makes it hugely beneficial to them when revision becomes an integral part of an essay assignment.  When an instructor and a well-crafted assignment guide them through the revision process, many students discover for the first time what they’re really capable of as writers.

Granted, extended revision can be difficult to integrate into classes that are not explicitly designed to teach writing. Many instructors simply don’t have the luxury of time necessary to collect full drafts, comment on them in any significant way, and repeat again with the final drafts.  Fortunately, this is not the only way to create space for revision. Consider these alternatives, none of which are mutually exclusive:

  • Simply spend some time in class discussing revision, giving students examples of what you would consider a solid first draft and solid final draft and offering various techniques for getting from the former to the latter (there are many, many resources for revision advice to be found online–find one that feels like the best fit for your assignment and point students there, or ask the WAC Director for suggestions).  Have students submit their rough drafts along with the final versions, and be sure to comment on how well the final draft improves on the earlier version in your feedback to the students.
  • Conduct a peer review session, either in class or asynchronously through the cloud, in which students comment on each other’s drafts.  When they submit their final drafts, ask students to address if and how they responded to their peers’ comments.
  • Set aside all or part of a class session on the day drafts are due, and have students evaluate their own drafts: what turned out well, what aspects are still in process, and how will they proceed with that knowledge? Have them turn this into a written revision plan, to guide the process to the final draft.
  • In a class with multiple essay assignments, require students to choose one essay to revise and resubmit at the end of the term.  You can allow the revised essay to replace the grade of the earlier version, or make it a separate assignment grade (both approaches have their own pedagogical merits).

Each of these techniques helps to make revision an integral part of the assignment, not a side practice that we might encourage, but not explicitly require. They also create opportunities for you as an instructor to step in and guide students’ revision processes, helping them to see methods and opportunities to improve their writing that they don’t.  While this kind of feedback does require some time and attention on the instructor’s part, it can often allow for less feedback (and easier grading) on the final version of the assignment, as you’ll already have created a dialogue with the students around the assignment that you can simply bring to a close with a few explanatory notes and (ideally) a completed rubric.

The concrete ways that revision complicates the use of AI writing tools are fairly obvious. Essays produced by ChatGPT tend to be remarkably free from grammatical errors, but fairly vacuous; thus, they require little editing but a great deal of revision. And, since it’s generally harder to revise someone else’s work effectively than your own, students who produce their drafts with ChatGPT will often find that the revision process requires more work for less return.

Again, though, the real value of guided revision is that it encourages students to see their writing as a work in progress and to get a better sense of what they can accomplish with a fully developed writing process. The more pride and value they associate with their own writing, the less likely they’ll be to let AI do it for them.

Encouraging Reflection and Metacognition

The WAC Program has (to understate it mildly) promoted reflective and metacognitive writing as a teaching practice for some time, so it’s probably no surprise to see a section on them here. In the context of creating AI-resistant writing assignments, though, reflective and metacognitive writing take on additional layers of value, both because they help students to see the benefits of their own intellectual work and because they make it more difficult to conceal if they’ve allowed AI to do that work for them. 

Quickly stated, the distinction between the two modes is that reflective writing looks backwards (what have I learned and experienced?), while metacognitive writing looks forwards (how can these experiences inform my future actions and methods?).  These modes of thinking go together more often than not, and both are obviously essential for learning.  By challenging students to enact these processes–to actively consider, in writing, what they’ve learned and what they plan to do with that learning–we help them to connect the disparate pieces of their education, understand their own strengths and weaknesses more effectively, and simply develop a better understanding of themselves.

In practice, this is often a much simpler and smaller-scale operation than those principles would suggest.  Any formal writing assignment can be scaffolded fairly easily with one or more informal reflective and/or metacognitive tasks.  These tasks can (and generally should) be short and fairly simple both to write and to read.  They can happen at any point in the writing process–before, after, or even while the student is writing the assignment itself.  Consider a few common scaffolding assignments in this vein:

  • A week or more before a formal writing assignment is due, ask students to write a paragraph or two summarizing their topic and articulating why they chose it (ideally, in the context of the ideas above, explaining how their assignment will respond to ideas that they have encountered in class).  Then, ask them to assess the work they’ll need to do to complete the assignment–what sources will they engage and how, what questions will they have to address, what conclusions do they still need to form, etc.?
  • The weekend before the assignment is due, ask students to write a concrete plan for their entire writing process. How long do they think it will take to create an outline, write a draft, revise it, and proofread it? When and where will they do this work? What additional help, if any, will they seek out, from whom, and when?
  • As they turn in the assignment, have students compose a short note to you about their writing process and how it worked out for them. How did their ideas change between conception and completion? What steps were easy or difficult? Did any of that surprise them? Overall, how happy are they with the final product as they submit it to you? Comment on this note as part of your feedback to the student — based on the final product, what aspects of the student’s writing process served them well, and what practices might they want to change or adopt for the next assignment?
  • As they begin work on the next assignment, ask students to consider what they learned writing the last one. What ideas from that project will inform this one? What lessons about their own writing process did they learn, and how will that inform the way they write this assignment?

None of these techniques are particularly novel, but they are powerful, because they require students to consider not just the final product of the assignment, but the actual learning process that the assignment is designed to enact.  It seems obvious to us that we create writing assignments because we want students to learn from the process of writing them, but students tend to fixate on the product rather than the process.  This makes the actual work of writing seem obscure and magical, even when they do it themselves. They sit down at the computer, mull over the topic at hand, and gradually, sometimes painfully, the final product grinds itself into being.  When they’re asked to articulate these steps in writing, though, their process becomes visible to them, and the opportunity emerges for them to critically examine what they do and how they can do it better.

Their writing process also becomes more visible to you, which is useful both instructionally and in the context of making assignments AI-resistant.  In instructional terms, metacognitive writing allows you to respond to the way students work, not just the work itself.  You can see how their ideas evolve and, ideally, help to guide them.  You can see how students approach writing (from methodologies of research to basic time management) and offer encouragement or guidance.  In other words, you can make their learning an active part of the class.

This pushes against the encroachment of AI in a number of ways.  Most concretely, any of the metacognitive steps suggested above could be completed effectively in class, making it much harder (though perhaps not impossible) for students to covertly hand the work over to the robot.  Furthermore, if students know that they’ll have to explain their writing process in some detail, they might think twice about letting AI do the writing.  None of the tasks suggested above should be particularly challenging for students who actually completed the assignment as designed, but they’re considerably more challenging as exercises in creative fiction.  Pointing this out to students when you introduce the assignment can reduce the perceived benefits of letting AI do the heavy lifting.

On a less concrete but more profound level, though, this kind of scaffolding discourages AI-based cheating by placing value on the writing process rather than just the product.  By helping students see how the real intellectual labor called for by your assignment is meant to benefit them, you discourage the transactional mentality that sees assignments essentially as invoices for students to fill.  My experience is that the overwhelming majority of students are more willing to work than we tend to assume, as long as they believe that their efforts will be rewarded in some way, and metacognitive writing can help them see the benefits of their work in a useful and tangible form.

The same can really be said for any of the practices described above. At the end of the day, we cannot force students to feel invested in their educations, nor can we stop them from seeking out ways to make their academic work easier, legitimately or otherwise. However, if we start from the assumption that most students are invested in their education and truly want to learn, and we create assignments that allow them to see and experience that learning as it happens, then the perceived value of letting AI do their writing for them will diminish significantly. It will also allow us, as instructors, to approach AI writing tools from a position of strength by making it a teaching issue rather than a detection and enforcement issue.

Generative Artificial Intelligence (GAI) Resource Guide for Faculty

  • Generative Artificial Intelligence
  • GAI and Personal Security
  • Strategies for Working with GAI
  • Assignment Design and GAI
  • Academic Integrity and GAI
  • GAI Resources and Help

Assignment Design and GAI

The opportunities and challenges associated with GAI are not much different from those faced by educators in the face of any other technology revolution. In recent times, educators have had to adapt to Google, Wikipedia, calculators, laptops and cell phones making their way into the classroom, bringing with them the potential for cheating. During the Covid-19 pandemic starting in 2020, many educators had to adapt to teaching fully online for the first time in their careers. At UMass Global, the Center for Instructional Innovation and the Library can help you design assignments that work harmoniously with new technology and minimize the potential for cheating. This page offers some advice about critical assignment design.

Page Contents:

General Advice for Creating Critical Assignments

Assignment methodology, sample assignments.

Creating prompts and assignments that lead students to produce the type of work we want to see has always been a challenge. Ideally, a well-constructed project should help connect students to their intended profession. These tips can help you write assignments that promote critical thinking, engage creativity, help students add their voice to their discipline, and hold them accountable for what they submit.

  • Write prompts to be as specific as possible.  For example, “ 'write 1000 words on Thelonious Monk's contributions to jazz' is more at risk of getting AI-generated responses than a paper that asks students to describe their experience listening to Monk’s music and how that experience reflects how he innovated music that came before or influenced music that they listen to today" ( Northwestern University , 2023 ).
  • Consider alternatives to a paper,  such as recording a video or a podcast or creating an infographic or website. This allows students to demonstrate their knowledge of a topic without relying on written assessments. This also accommodates students who may have difficulties with written assignments.
  • Scaffold every project. Have milestones that require frequent accountability (e.g., react to readings, develop and refine questions, write annotations, drafting, peer review, revising).
  • Require journaling at each stage of the process. Students should report in chronological order how they did the work, where they did it, what results they got at each stage, how they adjusted, whom they asked for advice and how they incorporated that advice, etc.
  • Ensure that all assignments give students an intriguing purpose to their work. This strategy boosts creativity and promotes critical thinking. See more on the RAFT method of assignment design below.
  • Consider adding a statement on GAI in your syllabus , such as this example from Dr. La Fontain-Stokes of the University of Michigan:

GAI programs have become more prevalent globally. Our class goal is to help you develop basic and advanced writing skills. In this sense, we are truly invested in seeing what your current writing and research skills are and working with you to improve and further develop these. GenAI can be a useful tool but should not replace your personal skillset and your ideas. You are responsible for all content (ideas, facts, citations) that appears in the work you submit for our class, however the work is generated. We highly discourage the uncritical use of GenAI, which can be very detrimental. GenAI currently makes mistakes, and at times makes up information, referred to as “ hallucinations .” The uncritical use of GenAI without attribution can be a violation of academic integrity and does not excuse you from inaccuracies in your work.

There are many good methods and theories about drafting prompts for academic projects. A well crafted assignment will foster critical thinking and reduce opportunities for any type of cheating. 

One method that is easy to emulate is the is RAFT Method of assignment construction as articulated in the book Engaging Ideas by John C. Bean . The RAFT model encourages task-based assignment writing that requires students to think and write in a way that models the processes and outputs of professionals in their discipline

Source: Bean, Engaging Ideas , 2nd ed., pp. 98-99.

Bean illustrates variations in assignment design with an example of the different ways in which a prompt could be written. As you read the examples below, Bean asks that we consider:

  • What differences in thinking are apt to be encouraged by each option?
  • What are advantages and disadvantages of each option?

With regard to GAI, we might further consider:

  • Which options have elements that you could allow students to enhance with GAI?
  • Which options have the most elements that are most likely to discourage cheating?
  • What should happen before any of these assignment variations is presented to students to ensure that they have the skills and confidence to complete it ethically and competently?

Source:  B ean, Engaging Ideas, 2nd ed., pp. 92-93. Click image to enlarge.

assignment of artificial intelligence

Here are samples of assignment prompts that either engage with GAI or make it difficult for GAI to generate successfully. These assignments should, of course, be "scaffolded," or preceded by prior exercises that prepare students for all the work necessary to complete it.

The background colors in the boxes below were selected by asking ChatGPT for the hex codes for light background colors.

General Assignment Prompts

Assignment Prompts that Engage with GAI

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10 Educational AI Tools for Students in 2024

Senior Content Marketing Manager

February 13, 2024

AI tools for students are becoming indispensable, from harnessing the power of artificial intelligence to refining writing nuances to getting instantaneous feedback on presentations. 

And the best part? Many of these groundbreaking AI writing tools are not just reserved for the elite—they are accessible and affordable, making them a boon for nonprofits and individuals on a budget. 

Whether you’re looking to craft impeccable essays or master the art of communication, dive in as we explore the top 10 AI-powered tools of 2024 tailored for your academic success.

What Are AI Tools For Students?

How students can best use ai, 2. quillbot, 3. gradescope, 4. otter.ai, 7. audiopen.ai, 9. smart sparrow, 10. wolfram alpha.

AI tools for students refer to various software and applications that utilize artificial intelligence to assist, enhance, or streamline the academic journey. Unlike conventional digital tools, AI-driven tools adapt, predict, and personalize learning experiences based on each student’s individual needs and patterns.

Here’s a breakdown of what they offer:

  • Personalized learning: Every student is unique, and the best AI-powered tool can recognize this. They adapt to each student’s pace, learning style, and preferences to deliver tailored content, be it in the form of reading material, quizzes, or tutorials
  • Instant feedback: Artificial intelligence education tools can analyze assignments, presentations, and projects, providing real-time feedback. This immediate response helps students understand their areas of improvement without waiting for teacher evaluations
  • Productivity enhancements: From organizing study schedules to setting reminders for assignment deadlines, AI tools are equipped to aid students in time management and organization, ensuring they stay on top of their academic tasks
  • Interactive learning: AI tools often come with chatbots, virtual assistants, and interactive platforms that make learning engaging and dynamic, breaking the monotony of traditional study methods.
  • Data-driven insights: These tools collect and analyze data based on a student’s performance, giving insights into strengths, weaknesses, and potential growth areas, aiding in better academic planning
  • Accessibility and Inclusivity: Many AI tools now offer features that make learning accessible for students with disabilities. From voice-to-text functionalities to visual aids, artificial intelligence ensures that education remains inclusive

AI can potentially transform a student’s learning trajectory, but the key lies in effectively harnessing its power. Here’s how students can make the most of AI:

  • Setting clear goals: Begin with a clear understanding of what you wish to achieve using SMART goals . Are you looking to improve your essay-writing skills, seeking help with math problems, or aiming to organize your study routine better?
  • Blend traditional with technological: AI tools offer unprecedented advantages but’re most effective when combined with traditional learning methods. For instance, after receiving feedback from an AI-powered writing tool, discuss it with a teacher or peer for additional insights
  • Engage in interactive learning: Use AI-driven interactive platforms, such as chatbots or virtual tutors. These platforms provide real-time engagement, making complex topics easier to grasp and retain
  • Analyze and adapt: Many AI tools provide data-driven insights about your learning patterns. Regularly review these analytics to understand your strengths and areas needing attention
  • Ensure data privacy: While using any AI tool, especially those requiring personal data or academic records, ensure that the platform respects user privacy
  • Stay open to feedback: One of the hallmarks of AI tools is instant feedback. Critiques, suggestions, or recommendations provided by these tools aim to enhance your skills and understanding
  • Stay curious: AI is a fascinating domain with limitless potential. Dive deep, explore its intricacies, and stay curious about how machine learning, neural networks, or natural language processing can further bolster your academic journey

10 Best AI Education Tools in 2024

In 2024, the educational landscape has been transformed by a slew of advanced AI-driven platforms. Here’s a curated list of the 10 best AI education tools leading the charge and revolutionizing learning experiences for students globally.

In the ever-evolving landscape of digital tools, ClickUp has emerged as a front-runner, not just as a conventional project management tool for students but also as an AI-driven powerhouse tailored for student success. Combining the best of organizational features with the prowess of an AI writing tool , ClickUp is redefining how students approach tasks, projects, and group collaborations.

With ClickUp’s education project management software , students can break down assignments into individual steps, which makes daunting projects more digestible, and then rank those tasks based on urgency and importance so they can focus on what matters most.

ClickUp’s collaborative tools turn group assignments from chore to joy thanks to real-time editing, advanced commenting features, and ClickUp Chat. ClickUp’s AI tools offer productivity analysis that suggests areas of improvement and optimizes your study routine.

In a nutshell, ClickUp isn’t just another project management tool; it’s a holistic AI platform tailored to fit the unique demands of student life. By leveraging its powerful AI writing tool features and the intelligence of AI, students can pave the way for academic excellence and a future of seamless project execution.

ClickUp best features

  • With ClickUp Tasks, students can organize assignments, projects, and other academic endeavors into manageable chunks
  • Automate routine tasks and set up workflows to streamline processes
  • All ClickUp Docs , discussions, and tasks are in one place, eliminating the hassle of toggling between multiple apps
  • ClickUp seamlessly integrates with various other tools and platforms that students commonly use, like Google Calendar, Drive, and even note-taking apps
  • Depending on a student’s preference, they can visualize tasks and projects as a list, on a Kanban-style board, or on a calendar, offering flexibility in project management styles
  • Monitor the time spent on different tasks or projects to ensure efficient time allocation
  • Never miss a deadline with ClickUp’s robust reminder system. Plus, get yourself organized with the ClickUp Class Schedule and Time Study Template
  • With AI enhancements, ClickUp adapts to a student’s work pattern, offering personalized suggestions and insights

ClickUp limitations

  • Some users have mentioned that while ClickUp is robust and feature-rich, it can initially come across as overwhelming, especially for those new to project management tools
  • While ClickUp’s desktop interface is widely appreciated, a few users have pointed out that the mobile app experience could be more streamlined

ClickUp pricing

  • Free Forever
  • Unlimited : $7/month per user
  • Business : $12/month per user
  • Enterprise : Contact for pricing
  • ClickUp AI: $5/month addon

ClickUp ratings and reviews

  • G2 : 4.7/5 (2,000+ reviews)
  • Capterra : 4.7/5 (2,000+ reviews)

Quillbot Dashboard

In an age where crisp, clear, and precise communication is paramount, QuillBot emerges as a beacon for students. A revolutionary AI tool for note-taking , QuillBot is specifically designed to enhance the quality and versatility of written content. 

Its intuitive interface and advanced algorithms make it a must-have for students striving for writing excellence. 

QuillBot acts as a second pair of eyes, helping students refine and elevate the standard of their written assignments and ensuring they make the best impression. Students can significantly expedite the editing and proofreading process with instantaneous suggestions and corrections, saving valuable time.

Knowing that their work has been reviewed and enhanced by a sophisticated AI-powered tool, students can confidently submit their assignments.

Over time, as students continually engage with QuillBot’s suggestions, they can organically improve their writing skills and internalize better writing habits. With its blend of advanced features and user-friendly design, it’s no surprise that many in the academic realm consider Quillbot an essential asset in their writing toolkit.

Quillbot best features

  • QuillBot offers a handy browser extension and integrates smoothly with platforms like Google Docs and Microsoft Word
  • Students can input sentences or paragraphs and receive alternative versions that retain the original meaning but use different phrasing
  • QuillBot offers many modes, including Standard, Fluency, Creative, and more, catering to various writing needs. Each mode provides a different spin on the content, allowing students to find the tone and style that best suits their work.
  • QuillBot can suggest synonyms for specific words, aiding students in diversifying their vocabulary and enhancing the richness of their content
  • Beyond restructuring sentences, QuillBot offers suggestions to correct grammar mishaps and enhance sentence fluency, ensuring that students’ work is both accurate and articulate

Quillbot limitations

  • Though QuillBot is adept at paraphrasing, there are instances where it might not fully grasp or retain the nuanced context of specific sentences, leading to suggestions that might be off-mark. For this reason, some people may prefer a Quillbot alternative
  • QuillBot offers both free and premium versions. Some students have noted that the free version, while useful, has limitations regarding word count and access to all features. This might necessitate an upgrade for those requiring extensive use.
  • On occasion, QuillBot might lean towards certain synonyms or phrasings more than others, leading to potential vocabulary repetition if not double-checked
  • For very intricate or specialized sentences, especially those related to specific academic or scientific topics, QuillBot might occasionally struggle to provide a satisfactory rephrase

Quillbot pricing

  • Premium: $19.95/month per user

Quillbot ratings and reviews

  • Capterra: 4.6/5 (100+ reviews)

Gradescope Dashboard

Grading and assessment, traditionally time-consuming and often subjective, have been given a 21st-century facelift with the introduction of Gradescope. This AI-enhanced platform has been specifically designed to streamline the grading process for instructors and provide valuable feedback for students.

With the detailed rubrics used in Gradescope, students clearly understand how they’re assessed, minimizing subjectivity and promoting fairness. Since instructors can grade similar answers in batches, students are ensured consistent feedback, even in large classes.

The efficiency of Gradescope means that instructors can grade assignments faster, leading to quicker feedback for students. The direct annotations on assignments allow students to see exactly where they excelled or where they need improvement, facilitating better understanding and growth.

Gradescope, with its blend of AI enhancement and user-centric design, is transforming the landscape of academic assessments. For students, it’s not just about receiving grades but gaining a clearer, more transparent insight into their academic progress and areas of growth.

Gradescope best features

  • Gradescope allows instructors to create detailed rubrics, ensuring that grading criteria are consistent and transparent. Once a rubric is set for a particular answer, it can be applied to similar answers, speeding up the process
  • The platform provides an easy-to-use interface where assignments can be scanned and uploaded. It supports various formats, making it versatile for different types of assessments
  • Instructors can provide specific feedback and annotations directly on the submitted work
  • Gradescope can analyze assignments to detect potential similarities with other submissions, helping uphold academic integrity
  • Gradescope can integrate seamlessly with popular LMS platforms, ensuring that grades and feedback are easily accessible to students

Gradescope limitations

  • For educators unfamiliar with the platform, setting up assignments and rubrics on Gradescope can take some time initially. It requires understanding the platform’s unique grading methodology and interface
  • If students’ written answers (for scanned assignments) are not clear or legible, the platform can struggle to identify and categorize them correctly, which can sometimes affect the grading process
  • While Gradescope’s AI-assisted grading is revolutionary, it works best for specific question types. More subjective or open-ended responses might not benefit as much from the batch grading feature
  • While Gradescope aims to integrate with many Learning Management Systems, occasional syncing issues or discrepancies might arise, requiring manual intervention

Gradescope pricing

  • Basic: $1/month per user
  • Team: $3/month per user
  • Solo: $3/month per user

Gradescope ratings and reviews

  • G2: 4.1/5 (6+ reviews)
  • Capterra: 4.7/5 (3+ reviews)

Check out these AI calendar tools !

Otter.ai Dashboard

In today’s fast-paced academic world, where lectures, seminars, and group discussions are the norm, having a tool to capture, transcribe, and analyze spoken content can be invaluable. Enter Otter.ai, a cutting-edge AI-powered transcription service that’s been a game-changer for countless students. 

By converting speech to text in real-time, Otter.ai doesn’t just transcribe; it transforms how students interact with auditory content. With Otter.ai, students can capture every word of a lecture, ensuring that no important detail is missed. Without the pressure of note-taking, students can be more present during lectures, focusing on understanding and internalizing content.

Transcriptions can be a valuable study aid, helping students review and revise their course content more effectively. For students with disabilities or non-native speakers, having a written transcript can immensely benefit comprehension.

Otter.ai stands out as a stellar AI tool, perfectly poised to meet the multifaceted needs of students. Whether revisiting complex lecture topics, collaborating on group projects, or ensuring that no spoken word gets missed, Otter.ai is a student’s trusty companion in the academic journey.

Otter.ai best features

  • Otter.ai can transcribe lectures, meetings, and conversations in real time, allowing students to focus on listening and engaging rather than frantic note-taking
  • Even in group settings, the platform can identify and differentiate between various speakers, ensuring that transcriptions are clear and organized
  • Transcriptions are easily searchable, allowing students to quickly locate specific topics, phrases, or sections of a lecture
  • Otter.ai can integrate with various platforms, like Zoom, to directly transcribe online lectures and meetings
  • The tool allows users to add custom vocabulary, ensuring that industry or subject-specific jargon is transcribed accurately
  • Transcriptions are securely stored in the cloud, ensuring that they’re easily accessible from any device
  • Students can effortlessly share their transcriptions with peers, making collaborative study sessions or group projects more efficient

Otter.ai limitations

  • Even though Otter.ai is one of the best in its field, no transcription service is flawless. Mispronunciations, heavy accents, or background noise can occasionally lead to transcription errors
  • For real-time transcription, a stable internet connection is crucial. A weak or fluctuating connection could disrupt the service
  • While the free version is useful, it comes with a monthly transcription limit, which might be restrictive for students with heavy usage

Otter.ai pricing

  • Basic : Free
  • Pro : $10/month per user
  • Business : $20/month per user
  • Enterprise : Contact Otter.ai for pricing

Otter.ai ratings and reviews

  • G2: 4.0/5 (100+ reviews)
  • Capterra: 4.5/5 (60+ reviews)

Knowji Dashboard

In the domain of language learning and vocabulary enhancement, Knowji stands out as a shining star. This AI-driven app combines the strengths of cognitive science, pedagogical principles, and cutting-edge technology to offer a unique learning experience. 

Tailored specifically for students, Knowji seeks to transform the often daunting task of vocabulary building into an engaging and productive endeavor. Through visual mnemonics and spaced repetition, Knowji ensures that vocabulary is learned and retained for the long term.

The app’s interactive interface and rich visual and auditory content make vocabulary learning more engaging and less tedious. The AI-driven adaptability ensures that students aren’t overwhelmed or under-challenged, making their learning journey more efficient and rewarding.

With contextual sentences, students gain a deeper understanding of how each word fits into everyday language.

Knowji isn’t just another vocabulary app; it’s a holistic learning ecosystem. With its fusion of AI technology, cognitive science principles, and pedagogical insights, Knowji positions itself as an indispensable tool for students aiming to expand their linguistic horizons.

Knowji best features

  • The audio features instill confidence in students, ensuring they can pronounce new words correctly
  • The student progress tracking features allow students to set and achieve vocabulary goals, giving them a clear sense of direction and accomplishment
  • Based on individual student progress and performance, Knowji’s AI algorithms adapt to provide learners with tailored content, ensuring they are always challenged at the right level
  • To provide context, words are paired with example sentences, helping students understand their practical application
  • Knowji uses the principles of spaced repetition, presenting words at optimal intervals to ensure long-term retention

Knowji limitations

  • Knowji’s vocabulary lists are curated for certain age groups and exam preparations. Some users might find the need for more advanced or diverse word lists, especially for specialized academic or professional usage
  • The app heavily relies on visual mnemonics and auditory cues. Students who don’t resonate with these learning styles might find other methods more effective
  • The rich visual and auditory content, while beneficial for learning, might consume a significant amount of device memory, especially if multiple-word lists are downloaded
  • No desktop version

Knowji pricing

  • Varies by module. Visit the Apple Store or Google Play Store for pricing

Knowji ratings and reviews

  • Capterra: N/A

OpenAI Dashboard

In the bustling world of artificial intelligence, OpenAI emerges as one of the industry’s foremost leaders. With its commitment to ensuring that artificial general intelligence (AGI) benefits all of humanity, OpenAI has released a suite of tools and platforms with immense potential for academic arenas. 

OpenAI’s offerings can be revolutionary for students, ushering in a new era of research, understanding, and knowledge dissemination. OpenAI’s extensive library of research papers and studies serves as a valuable reservoir of knowledge for students.

Language models like GPT can assist in drafting, editing, and even brainstorming, enhancing students’ writing and research capabilities.

OpenAI isn’t just a name in the AI industry; it’s a beacon of student progress, innovation, and responsible growth. For students, it presents a golden opportunity to be part of the AI revolution, ensuring they’re equipped, enlightened, and empowered for the challenges and opportunities of tomorrow.

OpenAI best features

  • Natural language processing makes it easy for students to get answers simply by typing in a question
  • Students can use Dall-E to create artwork for their presentations
  • OpenAI can adjust its tone, language, and sentence structure to fit the level of understanding a student has of the subject

OpenAI limitations

  • Even though OpenAI emphasizes ethical AI, students need to be cautious and educated about the potential misuse of such powerful tools, especially in academic settings, to prevent plagiarism or misrepresentation
  • Some of OpenAI’s advanced models, like the larger versions of GPT, demand significant computational resources for training, which might be out of reach for the average student

OpenAI pricing

  • Depends on the model used and a number of words produced. Visit OpenAI for pricing

OpenAI ratings and reviews

  • G2: 4.7/5 (300+ reviews)
  • Capterra: 4.4/5 (20+ reviews)

Audiopen.ai Dashboard

Navigating the digital age, students consistently search for tools to streamline their learning process and enhance productivity. Enter Audiopen.ai—a groundbreaking AI solution that magically transforms voice notes into refined, publish-ready text. Whether you’re a student drafting a research paper, pondering over a challenging essay topic, or simply brainstorming ideas, Audiopen.ai is here to redefine your content creation experience. 

Using Audiopen.ai, you can draft papers, memos, and emails in a fraction of the usual time, enhancing productivity. The tool makes it easy to navigate from vague ideas to coherent, publish-ready text effortlessly.

The tool’s adaptability to individual writing styles ensures the output reflects your authentic voice. Whether you’re verbalizing an email, text, or a blog post, Audiopen.ai is your one-stop solution.

Audiopen.ai best features

  • With tagging, your notes are always accessible and well-organized
  • The multilingual feature ensures non-native English speakers can equally benefit
  • Add in your specific vocabulary—perfect for proper nouns or academic terms
  • Whether you’re going for a casual email vibe or aiming for crystal clear precision, Audiopen.ai can adapt its writing style. There’s even an option for a customized style that mirrors your unique voice
  • The AI is designed to weed out filler words, repetitive phrases, and any incoherent segments, giving you concise and meaningful content
  • Web and mobile app availability ensures easy access from any device, anytime
  • It comes with a handy AI Chrome extension

Audiopen.ai limitations

  • The transcription time for the free version can feel limiting
  • This is a very new app, so some bugs remain. However, the developer is responsive to feature requests and concerns

Audiopen.ai pricing

  • Price: $75/year per user

Audiopen.ai ratings and reviews

Brainly Dashboard

Brainly is kind of like academic Reddit. Powered by students, experts, and academics around the globe, its database now boasts more than 250 million answers to every question under the sun.

Developed in collaboration with OpenAI’s GPT-4 technology, Brainly’s latest AI capabilities employ its vast knowledge base to furnish a more vibrant, tailored, and efficient learning journey.

Brainly’s enhanced features make AI-powered learning tools conveniently accessible, empowering students to seek homework assistance, enrich their subject comprehension, and bridge classroom knowledge gaps.

Brainly’s mission is clear: to bolster understanding and accelerate learning in this dynamic digital age.

Brainly best features

  • Learners can now opt for a “Simplify” function for straightforward explanations or “Expand” for a more thorough breakdown of topics. This allows students to customize the depth of their learning based on their individual needs
  • Students can now ask follow-up questions or request detailed explanations on previous responses
  • To ensure the reliability of information, Brainly incorporates expert moderators who oversee content quality
  • Beyond just finding answers, Brainly provides a platform for students to discuss, debate, and delve deeper into topics with peers from around the world
  • To encourage participation and consistent learning, Brainly has incorporated gamification elements like points and ranks
  • Brainly’s search functionality uses AI to deliver the most relevant answers, making the quest for information efficient

Brainly limitations

  • Since a significant portion of Brainly’s content is user-generated, there can be occasional discrepancies in the quality and accuracy of answers. While the platform does employ moderation, the vast number of contributions can lead to some incorrect or suboptimal answers slipping through
  • Even though Brainly operates in multiple languages and regions, the quality of content can sometimes vary across these languages, leading to discrepancies in the information available to students from different countries
  • Some of Brainly’s premium features are behind a paywall, which might not be accessible to all students

Brainly pricing

  • Plus: $24/year per user

Brainly ratings and reviews

  • G2: 4.0/5 (13+ reviews)
  • Capterra: 4.6/5 (20+ reviews)

Smart Sparrow Dashboard

Rooted in the mission of crafting unparalleled digital learning experiences, Smart Sparrow specializes in adaptive, simulative, and gamified learning environments. They are your partners in bringing visions to life, offering services from strategic consulting to custom software development.

Smart Sparrow uniquely marries the power of AI with the elegance of design.The editable templates and an extensive component library ensure that designing visually-rich, interactive courseware is a breeze.

With granular user permissions, collaborate on courseware creation with peers, colleagues, and external vendors.

Whether you deploy through your preferred Learning Management System or use Smart Sparrow’s platform, there’s flexibility at every step. Plus, with gradebook sync, keeping track becomes easier.

Smart Sparrow isn’t just another eLearning platform; it’s a movement towards more personalized, engaging, and innovative education technology. With its myriad features and a strong emphasis on user experience, it truly embodies the future of digital learning.

Smart Sparrow’s best features

  • The platform boasts a WYSIWYG (What You See Is What You Get) authoring tool, allowing educators and students to create stunning and impactful eLearning experiences effortlessly
  • With rich text editing, high-fidelity media import, and custom CSS, the potential for personalization is boundless
  • The built-in Analytics and reports go beyond mere grades, offering deep insights into student learning patterns, pinpointing problem areas, and understanding how students navigate and interact with content
  • From dragging and dropping elements, choosing from a vast array of interactive components, and customizing lesson plans, Smart Sparrow ensures a seamless digital journey
  • Create personalized lesson plans that students love. Import rich simulations, tasks, and activities that propel students to lean in and engage actively

Smart Sparrow limitations

  • The vast array of features and customization options can be initially overwhelming for users unfamiliar with eLearning authoring tools. It might take some time for educators, especially those less tech-savvy, to get comfortable navigating and maximizing the platform’s potential
  • Although Smart Sparrow boasts LMS integration capabilities, users may face occasional hiccups or compatibility issues when syncing with certain Learning Management Systems
  • Depending on the institution’s budget, the range of features and advanced capabilities might come at a price point that could be challenging for smaller institutions or individual educators
  • While the platform is mobile and tablet-ready, the experience on these devices might not always be as seamless or intuitive as on a desktop, especially when accessing more complex simulations or activities

Smart Sparrow pricing

  • Up to 5 learners: Free
  • Up to 100 learners: $15/user per course
  • Up to 500 learners: $12/user per course
  • 500+ learners: Contact Smart Sparrow for pricing

Smart Sparrow ratings and reviews

Wolfram Alpha Dashboard

The education landscape has experienced a paradigm shift with the advent of digital tools. Amidst a plethora of online resources, Wolfram Alpha stands out as an unparalleled computational knowledge engine. 

Designed to serve both students and professionals, it goes beyond simple search to offer answers to complex questions across multiple disciplines. Wolfram Alpha’s unique approach to online search is what sets it apart. Unlike traditional search engines that pull up web pages containing possible answers, Wolfram Alpha computes answers on the fly. This means students get direct and precise answers to specific questions, ranging from mathematical equations to historical data.

Whether you’re a history buff, a budding physicist, or a math enthusiast, Wolfram Alpha has you covered. It spans mathematics, science, engineering, geography, history, and music. Instead of just offering answers, Wolfram Alpha often provides additional information and related topics, encouraging students to explore further and satisfy their curiosity.

Wolfram Alpha is more than just a search engine. It’s a student’s companion in the journey of knowledge. Its computational prowess and extensive knowledge base ensure that students are not merely searching but truly understanding.

Wolfram Alpha’s best features

  • The engine understands natural language, which means students don’t need to phrase questions in a specific technical format
  • With dedicated apps for both iOS and Android, this virtual learning assistant is not confined to a desktop. Whether on a bus or in a cafe, students can have the vast computational power of Wolfram Alpha right in their pocket
  • Numbers and data come alive with Wolfram Alpha’s dynamic visualization capabilities. Graphs, charts, and other visual representations help students grasp complex concepts more intuitively.
  • For students grappling with math problems, Wolfram Alpha doesn’t just provide the answer but can also showcase a step-by-step breakdown of the solution, aiding in understanding and learning

Wolfram Alpha limitations

  • While Wolfram Alpha can handle a wide range of questions, there’s a learning curve involved in phrasing complex queries to get the desired result
  • It might not be the go-to tool for subjective topics, opinion-based questions, or areas that require human judgment
  • For highly specialized academic or research-based questions, the platform might sometimes provide a more generalized answer, which might not delve into the nuances a student or researcher might be looking for

Wolfram Alpha pricing

  • Basic: Free
  • Pro: $7.25/month per user
  • Pro Premium: $12/month per user

Wolfram Alpha ratings and reviews

  • G2: 4.7/5 (3+ reviews)

ClickUp: Acing The Test As The Premier AI Tool For Students

ClickUp brings to the table a suite of features specifically designed to adapt to the dynamic nature of student life. From group projects to thesis deadlines, ClickUp is the silent digital assistant every student wishes they had earlier. Its AI-driven insights help students prioritize, optimize, and visualize their academic journey, ensuring everything runs smoothly.

As we’ve navigated through various amazing AI tools that promise to revolutionize education, ClickUp stands a class apart. Not because it claims to do everything but because it delivers on its promise, ensuring students are equipped, empowered, and always ahead in their academic journey.

As the school year continues and challenges arise, students can rest easy knowing that with ClickUp, they’ve already secured an A+ in preparedness.

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Penn Engineering at University of Pennsylvania

Students earning a B.S.E. in AI are uniquely prepared to meet today's rapidly growing need for cutting-edge AI engineers.

The AI degree provides the mathematical and algorithmic foundations of AI techniques, along with hands-on experience in programming as well as using AI tools and foundation models. Complementing these engineering skills with a broader perspective, students learn about intelligence from a cognitive science perspective, and they develop a sense of the issues (and solutions) required to responsibly develop AI to benefit society.  Finally, students choose a concentration, ranging from machine learning, to vision and language, to data and society, to robotics, to AI and health systems. Interested students can learn more about the courses below by visiting the Penn Course Catalog .

Mathematics and Natural Science (7 CUs):

  • MATH 1400: Calculus, Part I
  • MATH 1410: Calculus, Part II
  • CIS 1600: Mathematics of Computer Science
  • ESE 2040: Linear Algebra 
  • ESE 3010: Probability or STAT 4300
  • ESE 4020: Statistics for Data Science
  • Natural science [1 CU, no lab requirement]

Computing (5 CUs):

  • CIS 1100: Python Programming
  • CIS 1200: Programming Languages
  • CIS 1210: Data Structures
  • CIS 3200: Algorithms
  • CIS 2450: Big Data 

AI (14 CUs):

Students choose at least one course unit from each of the following six categories:

Introduction to AI

  • ESE 2000: Data, Systems, Decisions
  • CIS 4210: Introduction to Artificial Intelligence
  • Machine Learning
  • CIS 4190: Applied Machine Learning
  • CIS 5200: Machine Learning

Signals & Systems

  • ESE 2100: Dynamic Systems
  • ESE 2240: Signal and Information Processing

Optimization & Control

  • ESE 3040 Optimization
  • ESE 4210 Control For Autonomous Robots

Vision & Language

  • CIS 5810: Computer Vision & Computational Photography
  • CIS 5300: Natural Language Processing

AI Project (must have AI development, 30% of grade from term project)

  • ESE 3060: Deep Learning: A Hands-on Introduction
  • CIS 3500: Software Engineering
  • ESE 3600: Tiny Machine Learning
  • ESE 4210: Control for Autonomous Robots
  • NETS 2120: Scalable and Cloud Computing

AI Electives:

In addition to the courses above, students will have an opportunity to take six AI courses selected from the list of approved courses below, along with the 1-year senior design sequence:

Machine Learning Electives

  • CIS 3333: Mathematics of Machine Learning
  • ESE 5460: Principles of Deep Learning
  • ESE 5140: Graph Neural Networks
  • ESE 4380: Machine Learning for Time-Series Data
  • ESE 6450: Deep Generative Models
  • CIS 6200: Advanced Deep Learning
  • CIS 6250: Computational Learning Theory
  • ESE 6740: Information Theory
  • CIS 7000: Trustworthy AI

Optimization, Systems, and Control Electives

  • ESE 3030: Stochastic Systems Analysis and Simulation
  • ESE 5000: Linear Systems Theory
  • ESE 5050: Control Systems
  • ESE 5060: Linear Optimization
  • ESE 6050: Modern Convex Optimization
  • ESE 6060: Combinatorial Optimization
  • ESE 6190: Model Predictive Control
  • ESE 6180: Learning for Dynamics and Control

Other AI Electives

  • MEAM 5200: Robotics
  • MEAM 6200: Advanced Robotics
  • ESE 6500: Learning in Robotics
  • ESE 6150: F1/10 Autonomous Racing Cars
  • CIS 4120: Human-Computer Interaction
  • CIS 5800: Machine Perception
  • CIS 5360: Computational Biology
  • BE 5210: Brain Computer Interfaces
  • CIS 4500: Databases
  • CIS 6500: Advanced Topics Databases
  • CIS 3990: Wireless and Mobile Sensing
  • NETS 3120: Theory of Networks
  • NETS 4120: Algorithmic Game Theory
  • ESE 4040: Engineering Markets

The above list will evolve as new courses are added to the program.

AI Concentrations:

The seven AI elective courses can be structured along AI concentrations depending on the interests of the student. Concentrations are optional and consist of four courses in a specific theme. 

  • Vision/Language
  • Data/Society
  • Health/Systems

Senior Design (2 CU):

Rather than offering a specific course for senior design, AI majors will embed themselves into the ESE, CIS or other Penn Engineering senior design courses. This will enable AI students to apply their AI skills across many engineering challenges.

Technical Electives (3 CU):

Three course units from Engineering, Math, Natural Science or from the list here .

General Electives (8 CUs):

Ai ethics: choose at least one of the following.

  • CIS 4230: Ethical Algorithm Design
  • LAWM 5060: Machine Learning: Technology Law

Cognitive Science Elective: Choose at least one of the following

  • COGS 1001: Intro to Cognitive science
  • LING 0500: Introduction to Formal Linguistics
  • LING 2500: Introduction to Syntax
  • LING 3810: Semantics I
  • PSYC 0001: Introduction to Experimental Psychology
  • PSYC 1210: Introduction to Brain and Behavior
  • PSYC 1340: Perception
  • PSYC 1230: Cognitive Neuroscience
  • PSYC 1310: Language and Thought
  • PSYC 2737: Judgment and Decisions
  • PHIL 1710: Introduction to Logic
  • PHIL 2640: Introduction to Philosophy of Mind
  • PHIL 4721: Logic and Computability 1
  • PHIL 4840: Philosophy of Psychology

Additional cognitive science courses may be taken and counted towards the SS/H electives.

  • SS/H Electives: Five course units, including a writing course. Three of these courses must be Social Science or Humanities courses, and 2 can be Social Science, Humanities, or Technology in Business & Society courses.
  • Free Elective: One course unit from free electives.
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Artificial Intelligence Assignment: Technical Intelligence Role In Digital Optimization

Assessment Task: The report is to be based on the following case study scenario about the use of Artificial Intelligence.

Artificial Intelligence (AI) includes groups of technologies that cover different fields such as machine and deep learning, predictive analytics, process automation, speech recognition, biometrics, and natural language processing. AI is seen by many businesses as the answer to increasing costs of human employment and used in a large number of industries in different ways. It has allowed the implementation of smart cities, developments in the medical sciences, special effects in movies and even the management of back-office type work. However, major concerns have been raised by many critics, some who are from the ICT fields themselves, that the use of AI must be controlled to prevent an unethical takeover by machines over humans.

You are the Head of ICT in a large logistics organisation with over 200 staff, established around 20 years ago. Your organisation’s head office is based in Sydney but it operates in various states of Australia and some countries in the Oceania region as well. Your organisation provides end to end logistics solutions to a large number of companies including warehousing, manufacturing and mining. Some of your client companies are expanding and they would like you to provide logistical solutions based on AI.

As a result, your organisation is now exploring options to expand the business in the next five years to include services based on AI. As a part of their expansion plans, the CEO of your organisation has asked you to investigate the technology and types of applications that can be used to provide services to your clients in the warehousing, manufacturing and mining industries. On the other hand, he wants to ensure that ethical limits of using AI are also observed with the use of AI. You have to complete this investigation in the next three weeks and draft a report with some recommendations for the next Executive Management meeting.

Your research and the subsequent report should cover the following tasks:

  • Definition of AI and the most up to date developments in the field that relate to logistics based solutions. This investigation must consider at least five different types of applications using AI. You should investigate examples from various industries within Australia and other parts of the world. Especially investigate the current uses of AI in logistics operations that service the transport, warehousing, manufacturing and mining industries. You could also include examples from other industries. Your report must identify actual examples of uses in the current market.
  • Based on the findings from the previous section, propose three AI based applications your organisation could use to expand its logistics business in the next five years. As a part of this analysis, consider the potential advantages and disadvantages of the applications you have investigated in the previous section, and the various risks (positive and negative) with respect to the solution/s you propose. When considering the potential advantages and disadvantages of the applications you have proposed, specifically explore them from an ethical, social and legal point of view. Ethics of AI in general and the specific applications you propose must be considered as a priority in this discussion. Relate this discussion to the proposed directions for your organisation.
  • Your analysis and proposed solutions in task 2 should provide three to five recommendations at the end of your report. Make sure that the specific recommendations have been evaluated as a part of your report discussion. The three AI applications and the ethical aspects of using AI must be considered in your recommendations. The recommendation section should directly focus on addressing the organisation’s current problem.

The report should be at a strategic level. It must not consist of highly technical or operational details as some of your Executive Managers are not from an information technology background.

Executive Summary This artificial intelligence assignment explores the technique that is widely used in the technical world. The use of artificial intelligence is also involved in the business field and for the establishment of the new business.

Therefore, Artificial Intelligence involves many technological features that cover all the different areas. Some of the areas that use artificial intelligence technique are like deep learning, predictive analytics, process automation, speech reorganization etc.

This technology gives the two main reasons for the use of this technique in the market and also its importance in the future scope:

  • The use of this technology artificial intelligence in the market for the increasing effects of human employment
  • This technology helps to develop the implementation of smart cities, developments in medical science, special effects in movies and also used in the business as a back office work.

There are some rumors about the use of artificial intelligence is that this technique controls to stop an unprincipled conquest by machines over humans. Artificial intelligence is also recognized as machine intelligence. It makes the machine capable of doing more work and helps human to solve the problem of doing hard work. Improvement in the field of artificial intelligence in the short term and in the long term goal provides the opportunities for the development of digitization in the digital market. Digital market improves the use of artificial intelligence to make the life more easy and interesting. This technique is recognized as a machine-friendly technology to make the work more in a simpler manner.

In addition, the importance of the technology and its advantage and disadvantage in the market for the future scope and improves the risk analysis of the Artificial intelligence in the digital world. Risk analysis with the artificial intelligence technique, includes the extra improvements in the field of robotics, job loss from AI, medicinal issues, accountability, supermodels like Sofia all these are the example of risks in the field of artificial intelligence in the future. In this report understand the role of artificial intelligence in the human’s life and its importance in the business marketing.

1.0 Introduction 1.1 Organizational Framework: This Artificial Intelligence Assignment assignments has been prepared by our IT experts from top universities which let ensures we deliver reliable IT management assignment help service. Technical Intelligence is the company based in Sydney (technical intelligence, 2005). This company is basically an artificial intelligence company that is used to develop the use of artificial intelligence models. The company currently works on the model of classroom automation. This model is in running stage and involves overall control of security systems, audio or video display, types of lightning, sensor etc.

The company TI introduces the projects on robots and other automation areas. This company uses the software according to the requirements. This company is recognized as the head of ICT in a large logistic organization. TI is having a collection of staff’s approx 200 and also has various branches in the other areas of Australia. This company provides end to end logistic company. The model of classroom automation is a good and defined project for the education system. Students get a large amount of profit through this project.

A great amount of positive feedbacks from the customers for the previous projects improves the development of the classroom model project. The future plan is simplified in two major sectors over the five years are:

  • Marketing strategy for the implementation of the products in the previous models or also in the current models
  • Improvement in the area of Artificial Intelligence in the automation area/ robotics that involves personal programs or healthcare issues

1.2 Objectives and Role: The main purpose of this artificial intelligence assignment is to give the reference of automation technologies in the TI, according to the flexibility of increased in the automation work in the business model with having an eye on the upcoming projects (Velik, 2012).

The main point or the goal of this report is to introduce about the artificial intelligence. This report also gives a brief notation on the automation area and their usefulness in the market. It also delivers the future scope of the company and the project model over five years that will take benefit from the ICT abilities to perform an advantage over the company's competitor.

Therefore, the above-mentioned features are the key objectives and rules for the development of the company.

1.3 Methodology: In order to study the concept of artificial intelligence, various journals, articles, reports and case papers have been used. Further, a wide search on the internet has also been carried out to learn about the current situation and future of the technologies as well.

1.4 Outline of the Report: The artificial intelligence assignment includes a widened study about artificial intelligence and its role in the logistics solutions. Further, five different types of AI has been used in this regards as well. In addition to investigating various examples in Australia and other areas across the globe, the project studies the contribution of AI across various industry fields. On the other hand, the report also proposes three AI-based applications for the chosen organization as well. Lastly, the report develops a list of recommendations in relation to the overall study.

2.0 Key Terminology 2. 1 Artificial Intelligence: This artificial intelligence assignment explores the use of machines that helps human for work in the business. It is a branch of science that deals with the use of machines which finds solution to multi-faceted problems in a more human-like style.

This usually includes the borrowing characteristics from human intelligence and applying them as algorithms in a computer-friendly manner. There are various different programs are introduced by the government for improving the works of artificial intelligence (Artificial Intelligence, 2018). Company use these government policies for the improvement or the development of the company

2.2 Automation: Automation is basically an allocation of human control purpose to control technological tools. Automation is the use of control systems and in arranges knowledge dropping the need for human involvement (Automation, 2018).

3.0 Current Application: 3.1 Reason or the Importance of AI: It is noted that Artificial Intelligence is an important part of the digital world. Computers are basically well used in performing automatic working out, using fixed program rules. This makes the artificial intelligence tools to execute simple droning tasks professionally and is also dependable in nature (Artificial Intelligence - Overview, 2018).

3.2 The Five Different Types of Applications Using AI: There are some applications of artificial intelligence are as follows:

  • Professional Arrangement
  • Natural Language Processing
  • Words Acknowledgment
  • Computer Apparition

Professional Arrangement: Professional Arrangement of a computer program is designed to react as a professional in a particular province. Theses professional arrangement is presently considered to support experts and not to replace the work. These arrangements are used in different areas like medical analysis, chemical examination and geographical explorations etc.

Natural Language Processing: Natural language processing terms as the language used in the technology should be understandable by the humans and the natural generation languages.

Words Acknowledgment: The basic method of interaction between the humans is the fetching of words. The main objective of words acknowledgment is to understand the methods and the concepts of the humans. Computers do not understand common languages, therefore to make a computer understand these acknowledgments of words this application is very important.

Computer Application: Computer application or the vision is used to sense their environment. The main object of this application is to give the vision to the computer to sense the things and these features are helping to make the computer understandable.

Robotics: Robotics is one of the biggest examples of Artificial Intelligence. A robot is classified as an electronic device that works on the algorithms or the commands of the programmer. Human tasks and the multi-functions are done by the programmed electronic device called robots. This is an interesting project of the Artificial Intelligence (McCarthy, 2007).

3.3 Uses of AI in logistics operations: There are three uses of AI in logistic operation are defined as follows:

  • Harnessing data
  • Customs Brokerage
  • Predictive Analytics

Harnessing Data: A clear concept of testing or analyzing the data from the supply chain is identified by the method of harnessing data. It is well-known fact that a high amount of data is analyzed that supply chain generates on daily basis. This information may be structured or in the informed manner, artificial intelligence will make these enable to the logistics of the industries to exploit it (How can Machine Learning boost your predictive analytics?, 2018).

Customer Brokerage: The AI can computerize and go faster to the brokerage of the procedure in civilizing the boundary of error and generate more charge economy.

Predictive analytics: It is a form of go forward analytics that uses both new and chronological data to predict activity, performance, and trends. It involves being relevant arithmetical analysis method (Patrick, 2018).

All these operations that service that service the transport, warehousing, manufacturing and mining industries.

4.0 Market Opportunities for TI 4.1 New Markets (Asia): Asia is recognized as a growing market that promotes artificial intelligence. It is noted that the use of the TI as can successfully expand into the nation and shall gain a lot of competitive edge factors with the help of the AI technologies. In addition to enhancing system automation, the technology enables fastening of operational processes as well. Further, the firm can successfully take of employee shortage situations. The integration of AI in the company shall also contribute towards enhancing product quality.

On the other hand, in countries like China, the government has taken a deep interest in investing towards increasing the presence of AI across the nation. It is noted that there exists a huge potential for business development in the market. Being a large country, firms tend to enhance building up competitive edges over rivals. This can be seen as an opportunity of AI in the nation.

4.2 Organizational Learning and Problem-Solving: Artificial Intelligence is a field of study dedicated to complex problem-solving. Learning in the area of AI is machine learning and its approaches. Deep learning is also one of the main features of the artificial intelligence. TI as a business can gain a lot from the features of the artificial intelligence, which are, involvement in the game factors, Image colorization is also included in the artificial intelligence feature, connections with transport systems, Generating images by text is also an example of artificial intelligence.

Speech or words, images, videos all are related to the artificial intelligence. Language issues and other kinds of reinforcements of learning generate the future of the artificial intelligence. There are various research has been organized for the wellness of the future scope of the AI. This is another factor that will act as an opportunity for TI.

The computer vision is also an important application of the artificial intelligence. This feature has many different kinds of tasks like image generation, image captions, image style transfers etc.

Artificial intelligence is all about the study of super-human style performance of the computer electronic device. Deep learning refers to the super-human quality on simple perceptual tasks. To understand human performance and estimate the followings are used for the device performance like object detection, face detection and emotional classification, food re-organization, activity reorganization, and the computer visions are examples of the artificial intelligence (Future of Artificial Intelligence – AI Scope and Career Opportunities, 2018). It is noted that all these characteristics shall significantly contribute towards enhancing systems and operational processes of the organization. Additionally, future scope of improved speech, voice and image quality with the improved electronic device will be taken into consideration by the management of TI for business growth and development. The personal assistants will become more personal and context awareness. More and more systems will run exactly on point.

5.0 Advantages and Disadvantages The company's strength in artificial intelligence is to introduce the new and modern featured models. The artificial intelligence assignment analyzes the needs and then develops the solution with the help of artificial intelligence. The working and introduction of the devices in this digital world for the collaborations of the systems enhance the artificial intelligence. This type of system is analyzed in the company present scenario. There are various different programs are introduced by the government to improve the works of artificial intelligence. The artificial intelligence assignment reviews government policies for the improvement or the development of the company. All the growth including the development of workers is dependent on the enhancement of the employee (Advantages and Disadvantages of Artificial Intelligence, 2018).

This technology is used in the internal market for reducing the hard work of the people. This technology introduces the era of digitalization in the market. The digital market is easy to use and fast to respond. This makes the system in a good and in a great way for the improvement of the knowledge of the new technologies (Technology and Innovation for the Future of Production:Accelerating Value Creation, 2017).

There are some advantages and disadvantages are described below:

5.1 Advantages

  • They will almost certainly be more and more used in the ground of medication
  • Knowledge-based expert system, which can cross-reference symptoms and disease will greatly improve the accuracy of diagnostics.
  • Object reorganization will also be a great aid to doctors,
  • Along with image from X-ray machines, they will be able to get a preliminary analysis of those images.
  • This, of course, will be possible only if people solve lawful questions that arise by giving power to a machine to control or influence the health of a human.
  • Improvement in the digital world
  • Make multiple profits by producing a number of products.
  • Reduce hard works of the common employees.

5.2 Disadvantage

  • Excess use of electronic devices
  • Low physical work, improve deceases
  • Hazardous to use the more electronic devices for the children
  • Create a gap between the human relations
  • Radiations and harmful materials harm the environment.
  • Chances of more radio-active decease problems.

6.0 Risk Analysis 6.1 Positive and Negative Views: There are some positive and negative views of artificial intelligence (The Positive and Negative Effects of Artificial Intelligence on Our Lives, 2018). Positive scenario is to take the company and the individuals in the digital world. Low-cost problems in doing any electronic programs. This artificial intelligence study introduced a good and powerful study for improving the work stamina and reducing human pressure. However, at the same time, there are some negative points also that concerned about the features of the system of AI. The negative impact of this study is the negativity of the minds of people to not work. Physical work is dropping down, which is harmful to the human health. The connection or the interaction between the people keeps going less. People have no times for others and take their decision individuals. These are the things which create negative impacts because of the excess use of Artificial intelligence.

6.2 Safety, Social and Ethical Deliberation: The Safety, social and the ethical consideration is very important for the human life security. There are some findings are allocated in the table below with problem types, causes and their responses (Dignum, 2018).

Table: Risk Analysis

6.3 Other Risks: There are some other risks in the use of artificial intelligence are described below:

Safety: The safety of the human life is very important and one of the important risk in the digital world of artificial intelligence. Safety is necessary for the use of electronic devices, however, these devices emit harmful radiations. Theses harmful radiations are hazards for the human health and also increase harmful radiations in the environment.

Lawful: All the works should be legal in the concerned with government policies. There are various discussions that raised the case of artificial intelligence and its use in the market. The government has made some rules and values and some limits for everything. Excess use of this gives harmful effects. During any wrong thing, the government has to take actions. Therefore, to use the work legally is very important.

Both this risk issues need to be taken care of by the management of TI business for the better results in the production as well as in the international market level (Application of Artificial Intelligence to Risk Analysis for Forested Ecosystems, 2018).

7.0 Conclusion The main motive of this assignment is to give the reference to the organization AI and to its management about the improvement of automation in the company in the market field. This artificial intelligence assignment describes all the issues of the artificial intelligence. The importance of artificial intelligence in the market and its uses. This report also describes the features of using digital devices and their usefulness in common lives. However, the harmfulness of the electronic devices and the excess use of it are also introduced.

This artificial intelligence assignment describes the role and the path to develop the technology in the market. A new introduction of artificial intelligence devices in the company TI improves the work efficiency. The enhancement of the company depends on the introduction of new technologies. These technologies help the company man for multiple productions at a single time.

The expansion of the company is based on the improvement of the dependencies of work on the machine. It gives relaxation to the people from the work and performs the task is a fast and ten time more speeds. This study gives both the positive and the negative points. It also encourages the gap between the interactions of the people.

The risk management is also very important while using artificial intelligence models. The concerned about the benefits and the security of the persons is also introduced in this report. The government policies and their limits are described and thus, this is also very important for the development of the technology in the company on the international market.

Therefore, this concludes that the excess use of the technology is harmful. This artificial intelligence assignment also explores the limited use of this study generates a good environment and produce a large amount of production in the company.

8.0 Recommendations 8.1 Market Expansion: It is recommended that the firm needs to undertake extensive marketing and advertising in order to ensure successful business expansion into nations like Japan and China. Further, the management of TI should also involve in organizing training sessions for the employees of the firm. This will help to enhance their technical know-how and thereby will allow the firm to integrate AI in a proper manner. Further, the production and sales department is suggested to learn about the needs and demands of the people in the two countries and thereby increase production and sales operations accordingly.

8.2 Implementation of AI: On the basis of this assignment, there are some recommendations that are recommended that TI Company and follow the strategies.

  • To use artificial intelligence technique, there are some importance measures should be follow like the safety, security and the limitation of the devices.
  • To improve the TI Company, use of automation is necessary. Therefore, use proper planning should be needed for the better result.
  • Automation techniques are use to improve the digital world.
  • Use legal policies and insure of the government limitations of the material use.
  • Invest in development artificial intelligence programs. And commands to improvements of the electronic devices like robots.
  • Customs brokerage
  • Predictive analytics

Reference Advantages and Disadvantages of Artificial Intelligence. (2018). Advantages and Disadvantages of Artificial Intelligence. Retrieved September 06, 2018, from content.wisestep.com: https://content.wisestep.com/advantages-disadvantages-artificial-intelligence/

Application of Artificial Intelligence to Risk Analysis for Forested Ecosystems. (2018). Application of Artificial Intelligence to Risk Analysis for Forested Ecosystems. Retrieved September 06, 2018, from ink.springer.com: https://link.springer.com/chapter/10.1007/978-94-017-2905-5_3

Artificial Intelligence - Overview. (2018). Artificial Intelligence - Overview. Retrieved from www.tutorialspoint.com: https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_overview.htm

Artificial Intelligence. (2018). Artificial Intelligence. Retrieved September 06, 2018, from www.sas.com: https://www.sas.com/en_us/insights/analytics/what-is-artificial-intelligence.html

Automation. (2018). Automation. Retrieved September 06, 2018, from www.inc.com: https://www.inc.com/encyclopedia/automation.html

Dignum, V. (2018). Ethics in artificial intelligence: introduction to the special issueEthics in artificial intelligence: introduction to the special issue. Retrieved September 06, 2018, from https://link.springer.com: https://link.springer.com/article/10.1007/s10676-018-9450-z

Future of Artificial Intelligence – AI Scope and Career Opportunities. (2018). Future of Artificial Intelligence – AI Scope and Career Opportunities. Retrieved September 06, 2018, from data-flair.training: https://data-flair.training/blogs/artificial-intelligence-future/

How can Machine Learning boost your predictive analytics? (2018). How can Machine Learning boost your predictive analytics? Retrieved September 06, 2018, from www.marutitech.com: https://www.marutitech.com/machine-learning-predictive-analytics/

McCarthy, J. (2007). Applications of AI. Retrieved September 06, 2018, from www-formal.stanford.edu: http://www-formal.stanford.edu/jmc/whatisai/node3.html

Patrick, K. (2018). 3 ways AI will upend logistics. Retrieved September 06, 2018, from www.supplychaindive.com: https://www.supplychaindive.com/news/artificial-intelligence-disrupt-logistics/521655/

technical intelligence. (2005). technical intelligence. Retrieved September 06, 2018, from www.thefreedictionary.com: https://www.thefreedictionary.com/technical+intelligence

Technology and Innovation for the Future of Production:Accelerating Value Creation. (2017). Technology and Innovation for the Future of Production:Accelerating Value Creation. Retrieved September 06, 2018, from www3.weforum.org: WEF_White_Paper_Technology_Innovation_Future_of_Production_2017

The Positive and Negative Effects of Artificial Intelligence on Our Lives. (2018). The Positive and Negative Effects of Artificial Intelligence on Our Lives. Retrieved September 06, 2018, from www.kibin.com: https://www.kibin.com/essay-examples/the-positive-and-negative-effects-of-artificial-intelligence-on-our-lives-bBzvJH0r

Velik, R. (2012). AI Reloaded: Objectives, Potentials, and Challenges of the Novel Field of Brain-Like Artificial Intelligence. Retrieved September 06, 2018, from www.researchgate.net: https://www.researchgate.net/publication/236130507_AI_Reloaded_Objectives_Potentials_and_Challenges_of _the_Novel_Field_of_Brain-Like_Artificial_Intelligence

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Revise Assignments in Response to Generative AI

Author: Laura Schmidli. Editors: Jonathan Klein & Molly Harris. Published on February 16, 2024.

As generative artificial intelligence (Gen AI) continues to become more sophisticated and ubiquitous, the utility of Gen AI tools, perspectives about their use, and cultural acceptance of them will continue to change. This landscape of larger societal change will continue to inform our context for teaching – including our course policies and assignments. This ongoing shift prompts a number of questions: 

  • What do we know about student and instructor perspectives on Gen AI?
  • How are these perspectives shaping classroom activities and vice-versa? 
  • What adaptations to assignments have been successful for instructors and students in L&S? 
  • How can we best prepare instructors for future semesters and students for their future beyond academia? 

In this article, we look closer at these questions, leaning on emerging research literature, examples from instructors in disciplines across L&S, perspectives from UW students, and considerations you might make in evaluating how Gen AI can support student learning in your context. 

The L&S Design for Learning Series in red with three red bars

Table of Contents

  • 1. Incorporate Gen AI to support learning outcomes and disciplinary practices. More
  • 2. Help students consider uses of Gen AI beyond writing assignments. More
  • 3. Help students consider Gen AI tools relative to future careers. More
  • Considerations for your own context More
  • Challenges and opportunities for the future More

What's Effective?

Understanding student perceptions of Gen AI in the classroom can help instructors design assignments that support learning outcomes while meeting students’ needs and appealing to their motivation. While some students are eager to engage with these technologies, others continue to have anxiety and uncertainty about their use. From recent research, we know that students with a good understanding of how these tools work may have less anxiety about them overall (Chan & Hu, 2023). But developing an understanding of these tools is not always easy and requires context. Gen AI tools have strengths and weaknesses, and critical thinking skills and disciplinary knowledge are necessary to evaluate their output (Dahlkemper et al., 2023). To add further complication, different instructors, even within the same discipline, may have different ideas about how Gen AI helps or hinders students in reaching their learning outcomes. For these reasons it is essential to help students use and evaluate Gen AI tools relative to your course and discipline. Providing students with practice using and evaluating Gen AI in your classroom  makes your expectations clear to students, provides students with evidence of Gen AI’s utility and limitations, helps students build skills, and encourages critical thinking about these tools.

1. Incorporate Gen AI to support learning outcomes and disciplinary practices.

Weaknesses and limitations of Gen AI tools have been widely documented. At times they generate inaccurate information, reproduce biases and stereotypes, and fabricate non-existent citations. They also use a linguistic style that indicates confidence, despite making errors. Using these tools effectively requires evaluating their output critically using background information and additional research. In other words, a disciplinary novice may need more support to interact with these tools compared to an expert. Providing students with opportunities to use and evaluate these tools within an assignment, at a level appropriate to their experience, can help them determine when these tools help or hinder their learning in your course and discipline. When the use of these tools supports specific course outcomes and learning goals, students will be better able to make sense of connections between GenAI tools, their course work, and their learning overall. This can also reinforce values related to academic integrity, knowledge production, and learning. 

Two examples below from L&S instructors highlight different ways they have incorporated Gen AI tools within assignments. In both examples, students are asked to incorporate Gen AI into an assignment in a specific way and evaluate its effectiveness within a disciplinary practice. Both examples also retain space for students to demonstrate creativity, develop research skills, and accomplish other goals central to the learning outcomes.

L&S Instructor Example

Portrait of Shanan Peters outdoors in a collared shirt

Shanan Peters, Professor, Geoscience

What did you do? I revised an assignment based around a traditional research paper on a scientific topic that honors students complete by incorporating use of ChatGPT. Students are expected to identify a viable research topic relevant to course content, and then select and gather information from traditional reputable sources on their own. Once they have completed these research steps, they then plan and execute prompts for ChatGPT that obtain additional explanation, clarify complex concepts, hypothesize potential scenarios, or further explore the topic and its implications. After engineering their prompts for the AI chat bot, students annotate and critique the exchange based on their background research and then reflect on the experience overall.

Why did you do it? When ChatGPT hit the scene, it was clear that this was going to be a powerful new tool that could facilitate some types of work. Perhaps more importantly, I was no longer confident that the traditional research paper would be an effective assignment, or that I would be able to consistently recognize ChatGPT-generated content and respond accordingly. So, I decided to tackle this new resource head on and incorporate it into the activity.

What impact did it have on students? Some students were very creative in their interaction with this AI tool. The feedback from students was generally positive. Most of them had little or no experience using it and some explicitly stated that they would begin using it more frequently for some tasks. Overall, students gained experience with both the utility and shortcomings of generative AI for basic research. As an example of the latter, ChatGPT happily responds, in a rather authoritative tone, to all manner of nonsense. After productively using the tool to start the assignment, one student really struck out to demonstrate just that, and did so spectacularly, with the system producing fantastical scenarios in response to probing prompts.

What might you change in the future? This first year had the advantage of novelty, for the students and me. Next year, that novelty will have likely worn off for most everyone involved. Nevertheless, the generative AI space is fast moving and the capabilities of the platforms are improving. I’m likely to try this type of assignment again, with a revised set of guidelines to foster even more critical assessment by students that is grounded in their traditional background research. One student also used the system to generate Julia code that attempted to reproduce and improve on an R simulation demonstrating selection that I showed in class. I liked this analytical bent and I might consider encouraging code generation to demonstrate a relevant concept as part of the assignment, though the very diverse backgrounds in this intro-level course would make that challenging as a general expectation.

Anna Andrzejewski with students at the Wisconsin Historical Society in 2019

Anna Andrzejewski, Professor of American Art and Architecture

What did you do? I asked students in my intermediate-level Frank Lloyd Wright class to use AI to create the “first draft” of their text for their research projects. Wright is an iconic, popular figure in architecture, and there is a great deal about him on the internet. Much of this is written by enthusiasts and is of questionable accuracy, so I thought it would be a good way for students to explore their “research chops” by measuring the accuracy of online information. They were to pick a building designed by Wright and ask the free version of Chat GPT to write a paper about that building relative to one of six course themes.

 Why did you do it? I was coming back from a year of research leave, and I had heard horror stories from my colleagues about how AI was affecting their teaching. I had not yet had any experiences in the classroom with students using AI on assignments, but I figured rather than lose sleep over people misusing it, I would try to see if there was a way to embrace it. I wanted to have students think about its benefits and drawbacks critically. My goal was partly to enlist students in the process of assessing where the technology is in my field, which is a field where information of all sorts circulates on the Internet. What impact did it have on students? The students were surprised how AI repeated many of the common ideas about Wright. Even if their building was not in the “prairie style” (just one phase of Wright’s long career), AI often repeated the idea that Wright was a prairie style architect. In one case, a student was working on a Wright-designed building that is well known by scholars, which AI insisted didn’t exist. They learned that AI could be a starting point for art historical research but had its limits. Many of the same ideas they had learned about in overview lectures and textbooks were repeated, without real attentiveness to their particular buildings. For some, it led them to the library or at least scholarly databases to find detailed information. In other words, most of what they got through AI remained surface level, leading them to go back to research resources to deepen their knowledge of their particular building and research topic.

What might you change in the future? I know AI will get better. With the technology in its infancy I expect the assignment will work differently in the future. I like the idea of using it for a draft, but I think I’ll have to be very specific about expectations to edit that draft in the future. For example, I’ll likely have to say “find three other research sources not listed in the draft” to supplement the work.

L&S Student Voices

“[Using AI in an assignment] was enjoyable and meaningful because it directed me to many resources and made connections when prompted appropriately, yet it began recycling its responses and gave misleading or false information when it could not locate something to fill the content gap. Consequently, I opted to abandon the AI output and instead crafted my own outline from scratch, focusing on what I deemed important. Utilizing notes, topics, and research citations from my Zotero manager, I developed a cohesive paper that reflected my own narrative voice that expressed my interests and ideas. While AI excels at information retrieval, it lacks passion and enthusiasm for the topic. Its approach was overly factual and robotic, lacking the spirit and enthusiasm I had for my project.” – Margaret Murphy, Art History student

“Seeing AI’s poor response motivated me to put more effort into the project to write a great essay. It did a good job of setting a quality standard that our projects needed to exceed. As a result, I conducted a much deeper literature review than I typically would for an essay.” – Jack, Art History student

2. Help students consider uses of Gen AI beyond writing assignments.

Many students express skepticism and uncertainty about Gen AI’s utility and appropriateness for college-level work (Baek and Tate, 2023). Furthermore, engaging with Gen AI in the context of a writing assignment contributes to this skepticism by  helping students see its weaknesses relative to their own skills (Tossell et al., 2024). However, students also report experimenting with Gen AI for tasks beyond writing assignments, including generating illustrations and other images, searching for and summarizing information, generating practice questions, generating topic ideas for assignments, offering suggestions for coding computer programs and scripts, analyzing data sets, and more. Determining when and how generative AI output can be useful for these tasks can require more specific evaluation skills and background knowledge.

In the example at the right, an instructor and students experimented with Gen AI as a partner in Socratic questioning during a class session. Modeling ways Gen AI can be effective, or not effective, during class can show students possible uses for it outside of the classroom, including for studying, testing their knowledge, or generating practice questions.

Jan Miernowski, Professor of French

What did you do? I assigned groups of 2-3 students to interact with ChatGPT, adapting a Socratic Tutor exercise shared by Jon Ippolito from the University of Maine Learning with AI webpage . Students asked ChatGPT to use the Socratic method to question the basis of their claims, where their interaction would consist of a series of claims and challenges. The students were supposed to make interpretative claims about Balzac’s novel ‘Le Colonel Chabert’ that we just finished analyzing in class.

Why did you do it? I wanted to see to what extent generative AI may replace my own interaction during the live class discussions in class. My teaching style is largely based on a guided questioning of students’ interpretative claims based on previously assigned readings.

What impact did it have on students? The exercise came on the heels of 2 weeks of discussions on the novel. At best, it served as a recapitulation and further training of the skill of interpretation of a literary text they read.

What might you change in the future? If I were to reuse this kind of prompt, I would make sure students’ initial statement is not equivocal so the machine is at least set on a reasonably correct path regarding the object of the exchange, and not allow it to go beyond 5-6 statements from the students. The exchange becomes increasingly idle after that.

3. Help students consider Gen AI tools relative to future careers.

Students are also concerned about the impact of Gen AI on their future careers (Chan & Hu, 2023; Tossell et al., 2024). Students have highlighted that its use may be prohibited in education but required later on the job. Therefore, helping students consider these tools relative to future roles as professionals, community leaders, and critical consumers of information is important. 

In the example below, an instructor engages students in productive conversation where students choose to reflect on their use of Gen AI related to their internships, classes, personal tasks, and future. The instructor is also transparent in their own use of Gen AI and joins students in reflecting on its use.

Portrait of Jennie Maunnamalai outdoors in a blue weater

Jennie Mauer Maunnamalai, Lecturer, La Follette School of Public Affairs

What did you do? Undergraduate students in my course are participating in a public policy internship and the course provides opportunities for reflection, analysis, and engagement with their classmates on their internship experiences. As part of my general course guidance on my syllabus, I included guidance allowing students to use AI as a tool and requiring them to properly cite its usage. Students in my course complete regular online discussion prompts designed to help them reflect on their internship experience along with their peers and additional written assignments that are submitted to the professor.  In several discussion prompts I modeled using AI to summarize a text and generate discussion questions. I also included a prompt one week focused on AI to help students consider how AI was (or was not) part of their career, academic, or personal lives and why. Students could choose which questions they wanted to respond to, in order to preserve flexibility and choice in what they shared.

Why did you do it? There is a lot of discussion about AI and I had assumed that students would be using AI in my course, their internship, other courses, and in their personal lives. I wanted to learn more about their usage of AI and how it was or was not showing up in other professional and personal activities. I also want to encourage the group to begin exploring and experimenting with using AI.

What impact did it have on students? I was surprised to learn that students were using AI tools less than I expected and that they shared many of the same concerns and anxieties I have seen with my own professional peers. I was impressed that students were thoughtful and measured in their thinking on AI and I hope that our continued group exploration of AI tools will expand our thinking.

What might you change in the future? I intend to become more familiar with using AI tools to better prompt students and to model some real usage. I also want to continue facilitating an environment where students can speak candidly about this emerging resource.

Considerations for Your Own Context

  • Bias and Stereotypes
  • Use as a Study Aid
  • Professional Use
  • Learning Goals
  • Original Thinking

We know that Gen AI output, whether text or images, reproduces biases and stereotypes. Many students also recognize that generative AI can perpetuate existing societal biases (Tossell et al., 2024). What might bias and stereotype look like within your particular discipline? How might you help students explore this further and think critically?

How are students engaging with Gen AI as a study tool in your courses? Consider if you want to provide students with guidance or practice in using Gen AI to aid their thinking – e.g., generating practice quiz questions using Gen AI.

Consider where you can transparently engage students in critical thinking around use of GAI in their future workplace or other aspects of their lives. How can skills and background knowledge needed to use Gen AI effectively in your course help students in future workplace situations? How can your own professional use of Gen AI tools inform students?

What do your learning outcomes tell you is most important in your course? What disciplinary practices and ways of thinking does your course support? Consider how student use of Gen AI might support these outcomes and practices, and where its use might hinder these outcomes. This can help you determine where you can incorporate Gen AI, versus where you can emphasize the value of original thinking for learning.

What existing assignments in your course do you think students are already using AI to complete? If these assignments are essential for students to complete on their own, consider student motivations and incentives. Are there ways you can better communicate and incentivize the value of original thinking and work in these assignments? Do these assignments clearly connect to your learning outcomes and are those outcomes compelling to students? Are students able to make mistakes without huge penalties? How might you transparently and proactively incorporate Gen AI into small parts of these assignments? How are students engaging with Gen AI as a study tool in your courses? Consider if you want to provide students with guidance or practice in using Gen AI to aid their thinking – e.g., generating practice quiz questions using Gen AI.

Challenges and Opportunities for the Future 

  • Just as instructors are concerned that reliance on Gen AI might erode critical thinking skills and creativity, so are students. Assignments revisions that focus student effort solely on evaluating AI-generated content, rather than creating their own content, may make students feel short-changed (Smolansky et al., 2023). Student comments in a recent study indicate that they appreciate assignments that preserve student creativity (Tossell et al., 2024). Yet many instructors remain concerned that students will rely on AI when asked to create their own content. How might we design assignments that preserve authentic student creativity while also discouraging misuse of Gen AI?
  • If GAI tools become more often correct or more nuanced and complex, will assignments that focus on correcting or evaluating GAI output remain compelling? In what other ways might we incorporate these tools?
  • GAI written output can appear confident, persuasive, and even empathetic at a surface level. How can we help students think critically about the tone, style, and rhetorical strategies when interacting with GAI chatbots?
  • Access to Microsoft Copilot through UW-Madison might help students and instructors who have privacy and intellectual property concerns, as institutional access provides greater security. However, this access doesn’t help with disparities in access if individuals can pay for more advanced tools than those provided by the institution. How might we support efforts to democratize access to these tools?

“I’m worried that using AI is a ‘slippery slope’. Grammerly could make my writing better, but will I unlearn how to write on my own? Other tools that write for you seem even worse. When is my work no longer really my own? Sometimes struggling with writing and revising over time helps me have better ideas and creativity. Not everything is supposed to be easy.” – Anonymous Student

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References & Further Reading

Amani, S., White, L., Balart, T., Arora, L., Shryock, Dr. K. J., Brumbelow, Dr. K., & Watson, Dr. K. L. (2023). Generative AI Perceptions: A Survey to Measure the Perceptions of Faculty, Staff, and Students on Generative AI Tools in Academia . https://doi.org/10.48550/ARXIV.2304.14415

Baek, C., & Tate, T. (2023). “ChatGPT Seems Too Good to be True”: College Students’ Use and Perceptions of Generative AI. OSF Preprints . https://osf.io/preprints/osf/6tjpk

Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology , 15 (3), ep430. https://doi.org/10.30935/cedtech/13176

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education , 20 (1), 43. https://doi.org/10.1186/s41239-023-00411-8

Dahlkemper, M. N., Lahme, S. Z., & Klein, P. (2023). How do physics students evaluate artificial intelligence responses on comprehension questions? A study on the perceived scientific accuracy and linguistic quality of ChatGPT. Physical Review Physics Education Research , 19 (1), 010142. https://doi.org/10.1103/PhysRevPhysEducRes.19.010142

Hou, I., Metille, S., Li, Z., Man, O., Zastudil, C., & MacNeil, S. (2024). The Effects of Generative AI on Computing Students’ Help-Seeking Preferences (arXiv:2401.02262). arXiv. https://doi.org/10.48550/arXiv.2401.02262

How AI reduces the world to stereotypes . (2023, October 10). Rest of World. https://restofworld.org/2023/ai-image-stereotypes/

Hutson, J., & Robertson, B. (2023). A Matter of Perspective: A Case Study in the Use of AI-Generative Art in the Drawing Classroom. The International Journal of New Media, Technology and the Arts , 18 (1). https://doi.org/10.18848/2326-9987/CGP/v18i01/17-31

In the Age of ChatGPT, What’s It Like to Be Accused of Cheating? (2023, September 12). http://drexel.edu/news/archive/2023/September/ChatGPT-cheating-accusation-analysis

Shoufan, A. (2023). Can students without prior knowledge use ChatGPT to answer test questions? An empirical study. ACM Transactions on Computing Education , 3628162. https://doi.org/10.1145/3628162

Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023). Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education. Proceedings of the Tenth ACM Conference on Learning @ Scale , 378–382. https://doi.org/10.1145/3573051.3596191

Tirado-Olivares, S., Navío-Inglés, M., O’Connor-Jiménez, P., & Cózar-Gutiérrez, R. (2023). From Human to Machine: Investigating the Effectiveness of the Conversational AI ChatGPT in Historical Thinking. Education Sciences , 13 (8), 803. https://doi.org/10.3390/educsci13080803

Tossell, C. C., Tenhundfeld, N. L., Momen, A., Cooley, K., & de Visser, E. J. (2024). Student Perceptions of ChatGPT Use in a College Essay Assignment: Implications for Learning, Grading, and Trust in Artificial Intelligence. IEEE Transactions on Learning Technologies , 1–15. https://doi.org/10.1109/TLT.2024.3355015

Van Campenhout, R., Hubertz, M., & Johnson, B. G. (2022). Evaluating AI-Generated Questions: A Mixed-Methods Analysis Using Question Data and Student Perceptions. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education (Vol. 13355, pp. 344–353). Springer International Publishing. https://doi.org/10.1007/978-3-031-11644-5_28

West, J. K., Franz, J. L., Hein, S. M., Leverentz-Culp, H. R., Mauser, J. F., Ruff, E. F., & Zemke, J. M. (2023). An Analysis of AI-Generated Laboratory Reports across the Chemistry Curriculum and Student Perceptions of ChatGPT. Journal of Chemical Education . https://doi.org/10.1021/acs.jchemed.3c00581

How to Cite this Article

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License . This means that you are welcome to adopt and adapt content, but we ask that you provide attribution to the L&S Instructional Design Collaborative and do not use the material for commercial purposes.

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Penn Engineering Blog

Posts from the School of Engineering and Applied Science

Penn Engineering Announces First Ivy League Undergraduate Degree in Artificial Intelligence

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The University of Pennsylvania   School of Engineering and Applied Science introduces its Bachelor of Science in Engineering (B.S.E.) in Artificial Intelligence (AI) degree , the first undergraduate major of its kind among Ivy League universities and one of the very first AI undergraduate engineering programs in the U.S. 

The rapid rise of generative AI is transforming virtually every aspect of life: health, energy, transportation, robotics, computer vision, commerce, learning and even national security. This produces an urgent need for innovative, leading-edge AI engineers who understand the principles of AI and how to apply them in a responsible and ethical way. 

“Inventive at its core, Penn excels at the cutting edge,” says Interim President J. Larry Jameson . “Data, including AI, is a critical area of focus for our strategic framework, In Principle and Practice, and this new degree program represents a leap forward for the Penn engineers who will lead in developing and deploying these powerful technologies in service to humanity. We are deeply grateful to Raj and Neera Singh, whose leadership helps make this possible.”

The Raj and Neera Singh Program in Artificial Intelligence equips students to unlock AI’s potential to benefit our society. Students in the program will be empowered to develop responsible AI tools that can harness the full knowledge available on the Internet, provide superhuman attention to detail, and augment humans in making transformative scientific discoveries, researching materials for chips of the future, creating breakthroughs in healthcare through new antibiotics, applying life-saving treatments and accelerating knowledge and creativity.

Raj and Neera Singh are visionaries in technology and a constant force for innovation through their philanthropy. Their generosity graciously provides funding to support leadership, faculty, and infrastructure for the new program.

“Penn Engineering has long been a pioneer in computing and education, with ENIAC, the first digital computer, and the first Ph.D. in Computer Science,” says Dr. Raj Singh, who together with his wife Neera have established the first undergraduate degree program in Artificial Intelligence within the Ivy League. “This proud legacy of innovation continues with Penn Engineering’s AI program, which will produce engineers that can leverage this powerful technology in a way that benefits all humankind.” “We are thrilled to continue investing in Penn Engineering and the students who can best shape the future of this field,” says Neera. 

Picture of Raj and Neera Singh sitting down

Preparing the next generation of AI engineers

The B.S.E. in Artificial Intelligence curriculum offers high-level coursework in topics including machine learning, computing algorithms, data analytics and advanced robotics.

“The timing of this new undergraduate program comes as AI poses one of the most promising yet challenging opportunities the world currently faces,” says Vijay Kumar , Nemirovsky Family Dean of Penn Engineering. “Thanks to the generosity of Raj and Neera Singh, Penn Engineering’s B.S.E. in Artificial Intelligence program, we are preparing the next generation of engineers to create a society where AI isn’t just a tool, but a fundamental force for good to advance society in ways previously unimaginable.”

Leading the program will be George J. Pappas , UPS Foundation Professor of Transportation in Penn Engineering. “Realizing the potential of AI for positive social impact stands as one of the paramount challenges confronting engineering,” says Pappas, a 2024 National Academy of Engineering inductee. “We are excited to introduce a cutting-edge curriculum poised to train our students as leaders and innovators in the ongoing AI revolution.”

Ivy League coursework equipping students for the future

The program’s courses will be taught by world-renowned faculty in the setting of Amy Gutmann Hall, Penn Engineering’s newest building. A hub for data science on campus and for the Philadelphia community when it officially opens this year, the state-of-the-art facilities in Amy Gutmann Hall will further transform the University’s capabilities in engineering education, research and innovation as Penn Engineering advances the development of artificial intelligence.

“We are training students for jobs that don’t yet exist in fields that may be completely new or revolutionized by the time they graduate,” says Robert Ghrist , Associate Dean of Undergraduate Education in Penn Engineering and the Andrea Mitchell University Professor. “In my decades of teaching, this is one of the most exciting educational opportunities I’ve ever seen, and I can’t wait to work with these amazing students.”

More details about the AI curriculum and a full list of courses available within the program can be reviewed here . 

“Our carefully selected curriculum reflects the reality that AI has come into its own as an academic discipline, not only because of the many amazing things it can do, but also because we think it’s important to address fundamental questions about the nature of intelligence and learning, how to align AI with our social values, and how to build trustworthy AI systems,” says Zachary Ives , Adani President’s Distinguished Professor and Chair of the Department of Computer and Information Science in Penn Engineering.

The B.S.E. in Artificial Intelligence program will begin in fall 2024, with applications for existing University of Pennsylvania students who would like to transfer into the 2024 cohort available this fall. Fall 2025 applications for all prospective students will be made available in fall 2024. Visit the new website at ai.seas.upenn.edu .

University Libraries

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Artificial Intelligence (AI) in Education

  • Class assignments and AI

Ideas to help design assignments

Recommended readings, references for assignment ideas.

  • AI and academic integrity
  • AI and ethics
  • Background Information
  • How to cite AIs

Attribution

This page is based on Chatbots & Critical Pedagogy from  AI in the Classroom . 

Move away from the five paragraph essay. Chatbots can follow this format easily. Encourage your students' originality by moving away from this formulaic format.

  • Tip: If you want to stick with the five-paragraph essay, test out your prompt on an advanced chatbot like ChatGPT. Greene (2022) writes, "If it can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped... if you have come up with an assignment that can be satisfactorily completed by computer software, why bother assigning it to a human being?"
  • Sticking with essays? Warner (2022) suggests focusing on process rather than product. Scaffolding learning and allowing students to explain their thinking and make learning visible along the way are strategies that may help you confirm student originality: "I talk to the students, one-on-one about themselves, about their work. If we assume students want to learn - and I do - we should show our interest in their learning, rather than their performance."

In the short-term, you can have your students  write essays in class and on paper . 

  • For longer research papers, students will have access to chatbots outside of class.
  • Students may need to use online resources for their writing.
  • You won't be able to use the LMS feedback tools for annotation, rubric scoring, and grading.
  • Note: Some students may have accommodations to type their work rather than handwrite it. Make sure to follow student accommodations when assigning work. 

Use  collaborative activities and discussion s to mitigate the use of chatbot responses in your class. While students may generate ideas from a chatbot, they will need to discuss with one another whether they want to use the chatbot responses, if they fit the prompt, and if they are factually accurate.

  • These strategies can work for online courses with a few tweaks. For discussions, ask students to post a recording rather than text. While students may generate a response using ChatGPT, creating their video will require more interaction with the content than copy-pasting a text response would.

Engage your students in  meaning-making activities  to demonstrate their learning. This could include: Skits*, Drawings and Sketches, Concept Mapping, Infographics*, Digital Storytelling*, or  Write* or revise Wikipedia articles  (Wiki Education). Other ideas from:

  • Let students choose a medium and activity  (“Digital Media Design Student Choice Board” by Torrey Trust is licensed under  CC BY NC SA 4.0 )
  • Fun formative assessment: 12 easy, no-tech ideas  (Ditch That Textbook)

* Note that a chatbot can provide an outline for these activities.

Brain dumps  are an ungraded recall strategy. The practice involves pausing a lecture and asking students to write everything they can recall about a specific topic. Read more at:

  • Brain Dump: A small strategy with a big impact  (Retrieval Practice)

During or after writing, students explain their process or thinking. Students could:

  • Use Comments in Word or Google Docs;
  • Create a video explaining their change history on a Google Doc;
  • Use Track Changes to show their revisions.

Consider using planned or impromptu oral exams. You may consider including phrasing in your syllabus about conducting oral exams if you suspect plagiarism through the use of a chatbot.

When selecting readings, consider sourcing more obscure texts for your students to read. Chatbots may have less information in their training data on obscure texts. As an example, the New York Times reports that, "Frederick Luis Aldama, the humanities chair at the University of Texas at Austin, said he planned to teach newer or more niche texts that ChatGPT might have less information about, such as William Shakespeare’s early sonnets instead of 'A Midsummer Night’s Dream'" (Huang, 2023). 

(Note that ChatGPT is currently trained on data through 2021. Some educators suggest using newer writings and research, but this strategy isn't foolproof since the training models for chatbots are updated frequently.)

Field Observations : Coordinate times to take your class to conduct field observations; students can note their observations and write a reflection about their experience.

  • A Teacher's Prompt Guide to ChatGPT Created by Centre for Education Statistics and Evaluation, New South Wales, Australia
  • Critical Questions about Technology We encourage you to approach chatbot tools with a critical lens before structuring course assignments with these tools. Some students may be unaware of these tools and what they can do, and others may only be thinking about how they can benefit from the tool.
  • ChatGPT and Assessment by Ean Henninger, UNM Office of Assessment
  • Teaching With and About AI By Lori Townsend, University Libraries
  • Aaronson, S. (2022, November 28).  My AI safety lecture for UT Effective Altruism .  Shtetl-Optimized: The blog of Scott Aaronson .
  • Bowman, E. (2023, January 9).  A college student created an app that can tell whether AI wrote an essay . NPR .
  • Caines, A. (2022, December 29).  ChatGPT and good intentions in higher ed.   Is a Liminal Space .
  • Caren, C. (2022, December 15).  AI writing: The challenge and opportunity in front of education now . Turnitin .
  • Chechitelli, A. (2023, January 13).  Sneak preview of Turnitin’s AI writing and ChatGPT detection capability . Turnitin .
  • Ditch That Textbook . (2022, December 17).  ChatGPT, chatbots and artificial intelligence in education .
  • Hick, D.H. (2022, December 15).  Today, I turned in the first plagiarist I’ve caught using A.I. software to write her work  [Facebook post]. Facebook .
  • Huang, K. (2023, January 16).  Alarmed by A.I. chatbots, universities start revamping how they teach . New York Times .
  • Greene, P. (2022, December 11).  No, ChatGPT is not the end of high school English. But here’s the useful tool it offers teachers . Forbes .
  • Kelley, K.J. (2023, January 19).  Teaching actual students writing in an AI world . Inside Higher Ed .
  • OpenAI. (2022, December).  ChatGPT FAQ .
  • Trust, T. (2023).  ChatGPT & education  [Google Slides]. College of Education, University of Massachusetts Amherst.
  • Warner, J. (2022, December 11).  ChatGPT can't kill anything worth preserving: If an algorithm is the death of high school English, maybe that's an okay thing .  The Biblioracle Recommends .
  • Watkins, R. (2022, December 18).  Update your course syllabus for chatGPT . Medium .
  • Wiggers, K. (2022, Decemer 10).  OpenAI’s attempts to watermark AI text hit limits . TechCrunch .
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  • Last Updated: Nov 20, 2023 4:43 PM
  • URL: https://libguides.unm.edu/AIinEducation

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Use of Generative AI in Assignments (U of T Syllabus Language)

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Feb. 14, 2024

In Vietnam, VCU professor shares insight into how artificial intelligence can support teaching languages

On assignment from the state department, robert godwin-jones led workshops at three universities and addressed the ‘mixed feelings’ about ai in the classroom., share this story.

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By Joan Tupponce

Instead of sitting down for a traditional Thanksgiving meal this past November, Robert Godwin-Jones headed to Vietnam. Tapped by the State Department, the Virginia Commonwealth University professor served as an English Language Specialist who shared his expertise in how artificial intelligence can enhance the teaching of languages.

“I’ve been interested in AI since it has exploded in availability, interest and capabilities,” said  Godwin-Jones , Ph.D., a professor and languages specialist in VCU’s  School of World Studies  in the  College of Humanities and Sciences .

The State Department contacted him in October about going to Vietnam for three weeks to teach two-day workshops on how to integrate AI into language teaching. He led sessions at three universities— the University of Economics in Ho Chi Minh City, Hai Phong University of Management and Technology and the Ha Long University.

Class participants included English teachers at primary, high school and university levels and graduate students aspiring to be teachers. During the workshops, teachers were asked to take examples from their own courses, possibly from their textbooks, and create – with the help of AI programs – lesson plans or learning materials.

“The teachers had mixed feelings,” Godwin-Jones said. “They were nervous about using AI because they thought it would give students the opportunity to cheat.”

To address this, Godwin-Jones demonstrated ways to use AI in responsible and ethical ways.

“I told them to find assignments where AI won’t work, like reporting on a personal experience or an assignment in creative and collaborative writing,” he said. “Eventually they saw that it could be useful in developing teaching material and giving students the ability to try out or rehearse a language.”

AI has been shown to be useful for foreign language learners and teachers in a variety of ways. Among them, it can serve as a conversation partner for practicing the target language in written or spoken form.

“The mobile app ChatGPT, for example, has voice capabilities in multiple languages, with very human-sounding synthetic voices able to understand and speak on an endless variety of topics,” Godwin-Jones said. “It can be instructed to use the language at a particular level of proficiency, matching that of the learner.”

AI can handle the mechanics of language but not “the nuances of social language use,” he added.

“Human language is based on repeated patterns and regularities, which is why machine learning has been so successful. But human behavior and social language use are unpredictable. We adjust our language use on the fly to fit the conversational context,” Godwin-Jones said. “Developing interactional competence is a major component of language learning, and it is achieved through repeated social interactions.”

Albeit a valuable resource in the classroom, AI tools “are not an effective alternative to human teachers,” he said.

Godwin-Jones added that he enjoyed his time in Vietnam.

“It was my first time there. It was a great experience culturally, and the people were quite friendly,” he said. “The best thing for me in the whole process was socializing with the teachers. I learned about life in Vietnam. I felt very welcomed.”

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United States Patent and Trademark Office - An Agency of the Department of Commerce

USPTO issues inventorship guidance and examples for AI-assisted inventions

Published on: 02/12/2024 16:44 PM

Additional information about this page

IMAGES

  1. Fundamentals Of Artificial Intelligence

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  2. Artificial Intelligence Essay

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  3. AI Assignment

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  4. NPTEL An Introduction to Artificial Intelligence Week 2 Assignment

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  5. Professional Artificial Intelligence Assignment Help

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  6. Artificial Intelligence in Education Archives

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VIDEO

  1. Learn Artificial Intelligence

  2. Artificial Intelligence and its Applications

  3. Artificial Intelligence

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  5. artificial intelligence || lecture 8

  6. An Introduction To Artificial Intelligence

COMMENTS

  1. PDF AN INTRODUCTION TO ARTIFICIAL INTELLIGENCE

    What is Artificial Intelligence? AN INTRODUCTION TO ARTIFICIAL INTELLIGENCE COMPILED BY HOWIE BAUM • Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals, such as "learning" and "problem solving. .

  2. Assignment 1: Recent Advances in AI Essay

    Computer Science 311 - Assignment 1: Recent Advances in AI Essay. Martin has 22 years experience in Information Systems and Information Technology, has a PhD in Information Technology Management ...

  3. Introduction to Artificial Intelligence (AI)

    There are 4 modules in this course. In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get ...

  4. Integrating AI into assignments

    Creating your course policy on AI An effective syllabus works to motivate learning, define goals, explain course structure, and provide support to students as they learn. Your course policy on AI in your syllabus should: Be clearly stated and specific Clarify the context or conditions of allowable AI use

  5. Class assignments and AI

    Where an assignment requires an AI source to be cited, you must reference all the content from tool that you include in your assignment. Failure to reference externally sourced, non-original work can result in scholastic dishonesty.

  6. Artificial intelligence (AI)

    artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

  7. Generative AI in your assignments

    Artificial Intelligence (AI) Can I use generative AI tools in my assignments? Some subject coordinators may explicitly include information in your assessment guidance as to whether these kinds of tools may be used and how. You must comply with the requirements of the assessment task - if you are unsure, check with your lecturer.

  8. PDF Assignment 1

    1. "Machine learning is an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience.

  9. AI in Assignment Design

    AI in Assignment Design Using generative artificial intelligence (AI) can be both productive and limiting—it can help students to create and revise content, yet it also has the potential to undermine the process by which students create.

  10. Model ai assignments 2020

    The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and ...

  11. Artificial Intelligence and Assignment Design

    Considerations for Developing an AI Assignment Alignment with Your Course Goals In the development of AI assignments, the primary consideration is whether the use of AI will help your students achieve the learning goals of the course. Ask yourself, does this assignment help student gain skills and knowledge central to your course and field?

  12. Model AI Assignments 2020

    The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and ...

  13. AI Guidance & FAQs

    AI Pedagogy Project. Visit the AI Pedagogy Project (AIPP), developed by the metaLAB at Harvard, for an introductory guide to AI tools, an LLM Tutorial, additional AI resources, and curated assignments to use in your own classroom.The metaLAB has also published a quick start guide for Getting Started with ChatGPT Teaching and Artificial Intelligence. A Canvas module, created by the Bok Center ...

  14. Model AI Assignments

    Venue The Model AI Assignment Session is a part of EAAI-24: The Fourteenth Symposium on Educational Advances in Artificial Intelligence. This will be held February 24-25, 2024 in conjunction with AAAI-24 in Vancouver, British Columbia, Canada.

  15. Assignments: Artificial Intelligence at Northwestern

    Conditional or Open: Permitting or Requiring GAI Use If your course is open or if you choose conditional use of GAI for your course, consider the following elements when permitting or requiring students to use GAI on assignments: Students should only be required to use what is available and free

  16. Artificial Intelligence (AI), Features, Type, Application, Advantage

    Generative Artificial Intelligence, often referred to as Generative AI, is a subset of artificial intelligence focused on creating systems capable of generating content autonomously. These systems use techniques such as neural networks, deep learning, and reinforcement learning to produce text, images, music, or other data types.

  17. AI-Resistant Assignments

    Making revision integral to the assignment helps students critically examine and improve their writing process, while making it harder to take AI-assisted shortcuts. Reflective and metacognitive writing assignments make students' learning visible to them and to you; it also holds students accountable for the intellectual work of your course ...

  18. Assignment Design and GAI

    Generative Artificial Intelligence (GAI) Resource Guide for Faculty. This page will help faculty learn about GAI, use it effectively, and prevent cheating with it. ... General Assignment Prompts. Interview a military member or veteran about their military experience and record it in either an audio or video format. Identify basic demographics ...

  19. 10 Educational AI Tools for Students in 2024

    AI tools for students are becoming indispensable, from harnessing the power of artificial intelligence to refining writing nuances to getting instantaneous feedback on presentations. ... Instant feedback: Artificial intelligence education tools can analyze assignments, presentations, and projects, providing real-time feedback. This immediate ...

  20. Artificial intelligence assignment with images , examples and ...

    Similarly in artificial intelligence reasoning process involves, a machine to choose a right algorithm to ensure that they. provide the most possible accurate results. It is important in artificial intelligence as we want the machine to things like human brain, which can also say that "Reasoning is a way to infer facts from existing data".

  21. Curriculum

    The AI degree provides the mathematical and algorithmic foundations of AI techniques, along with hands-on experience in programming as well as using AI tools and foundation models. Complementing these engineering skills with a broader perspective, students learn about intelligence from a cognitive s

  22. Artificial Intelligence Assignment: Technical Intelligence

    This artificial intelligence assignment explores the technique that is widely used in the technical world. The use of artificial intelligence is also involved in the business field and for the establishment of the new business. Therefore, Artificial Intelligence involves many technological features that cover all the different areas.

  23. Revise Assignments in Response to Generative AI

    Author: Laura Schmidli. Editors: Jonathan Klein & Molly Harris. Published on February 16, 2024. As generative artificial intelligence (Gen AI) continues to become more sophisticated and ubiquitous, the utility of Gen AI tools, perspectives about their use, and cultural acceptance of them will continue to change. This landscape of larger societal change will continue to…

  24. Assignment On Artificial Intelligence PDF

    Assignment On Artificial Intelligence PDF | PDF | Artificial Intelligence | Intelligence (AI) & Semantics ASSIGNMENT ON ARTIFICIAL INTELLIGENCE.pdf - Free download as PDF File (.pdf), Text File (.txt) or read online for free.

  25. Penn Engineering Announces First Ivy League Undergraduate Degree in

    Amy Gutmann Hall (Photo courtesy of Eric Sucar) The University of Pennsylvania School of Engineering and Applied Science introduces its Bachelor of Science in Engineering (B.S.E.) in Artificial Intelligence (AI) degree, the first undergraduate major of its kind among Ivy League universities and one of the very first AI undergraduate engineering programs in the U.S.

  26. Class assignments and AI

    During or after writing, students explain their process or thinking. Students could: Use Comments in Word or Google Docs; Create a video explaining their change history on a Google Doc; Use Track Changes to show their revisions. Consider using planned or impromptu oral exams.

  27. Use of Generative AI in Assignments (U of T Syllabus Language)

    Use of Generative AI in Assignments (U of T Syllabus Language) By wsg_admin / 17 November 2023 . Post navigation.

  28. In Vietnam, VCU professor shares insight into how artificial

    Feb. 14, 2024. In Vietnam, VCU professor shares insight into how artificial intelligence can support teaching languages On assignment from the State Department, Robert Godwin-Jones led workshops at three universities and addressed the 'mixed feelings' about AI in the classroom.

  29. USPTO issues inventorship guidance and examples for AI-assisted

    USPTO issues inventorship guidance and examples for AI-assisted inventions. To incentivize, protect, and encourage investment in innovations made possible through the use of artificial intelligence (AI), and to provide the clarity to the public and United States Patent and Trademark Office (USPTO) employees on the patentability of AI-assisted inventions, the USPTO has published guidance in the ...