MIT

Generative AI tools can support many types of work at MIT. However, using these tools requires care — especially when working with Institute data. MIT work should be conducted using MIT-licensed AI tools whenever Institute data is involved.

The Institute's data classification framework is found on the Infoprotect website. Use the self-assessment tool and see the risk level definitions with examples to determine the risk level of your data.

| Publicly Available GenAI Tool | | MIT-Licensed GenAI Tool | | Low Risk Data | | Not recommended* | | Allowed | | Medium Risk Data | | Not allowed | | Allowed | | High Risk Data | | Not allowed** | | Not allowed** | *For MIT-related work, low-risk information should be used only in MIT-licensed generative AI tools. Public generative AI tools may use submitted data for model training or external sharing and are not governed by MIT agreements. MIT provides licensed tools , such as Parley , that are designed to support Institute work while protecting MIT data.

**Do not use high-risk information with Generative AI tools, including MIT enterprise Generative AI tools. If you have used high-risk data in a Generative AI tool, contact security@mit.edu immediately.

If you are unsure if an AI tool is right for your data’s risk level, contact ai-guidance@mit.edu .

MIT enterprise agreements govern the safety and security of data entered into MIT-approved tools. A list of Generative AI tools currently licensed by IS&T can be found on the IS&T website.

If you wish to purchase new Generative AI tools, you must go through the VPF procurement process to ensure the vendor agreement has the appropriate terms. IS&T works closely with the MIT IT Governance Committee , Information Technology Policy Committee , and the Office of General Counsel to determine appropriate terms for vendor agreements relating to generative AI.

MIT's Chair of the Faculty has recommended the use of these resources on generative AI and teaching provided by the Teaching and Learning Laboratory. This guidance does not address all issues for consideration when using generative AI tools as part of MIT research, nor does this guidance focus on when MIT researchers are creating generative AI tools or publicly releasing datasets/information that could become ingestible data for use by MIT researchers or third parties for training generative AI tools. Please contact mitogc@mit.edu for further guidance on research-related applications or development of AI tools.

Researchers should also refer to the COUCHES Guidance on AI in Research and the MIT Libraries Guidance on Citing AI-Generated Content .

IS&T's list of generative AI tools available at MIT

Report of the Working Group on Artificial Intelligence in Administration and Operations

Related MIT Policies

• 9.2 Personal Conduct and Responsibilities Towards Students and Employees
• 10.0 Academic and Research Misconduct and Dishonesty
• Academic Integrity at MIT
• Policies Regarding Student Behavior
• Responsible and Ethical Conduct at MIT

MIT’s Written Information Security Program

• Information Protection at MIT

Intellectual Property

• Patenting and Commercializing Your Invention

Patent Applications and Invention Disclosures

• Technology Licensing Office Guidelines for Using Generative AI in Patent-Related Activities

Publishing

• Scholarly Communications: Publishing and Research
• Citing AI tools

Sponsored Research & Proprietary Information of Third Parties

• Industry Sponsored Research Agreements

Page updated 6/23/2026

• Current official hub: https://tll.mit.edu/teaching-resources/course-design/ai-in-teaching-learning
• Source-history note: The excerpt originated on an earlier TLL subpage that was no longer available when rechecked on 2026-08-29; the current official hub above replaces the stale link.
• Domain: tll.mit.edu

The sheer daily volume of new information and insights about Generative AI (genAI) makes it imperative for all of us to set aside regular periods of time to reflect 1 on this powerful tool. We hope you’ll take time to develop your own reflections as well as discuss with your colleagues and students the successes and challenges you encounter, as well as identify lessons learned, and brainstorm about potential modifications for future semesters.

To help MIT faculty & instructors navigate the sea of information on the use of genAI in higher education 2 , TLL has combined timely news, advice, and resources on genAI with best practices in teaching and learning. As you design and deliver your subjects in this rapidly changing world of genAI, we recommend that you engage with the following:

• Refine your goals for learning
• Be clear about your expectations and the acceptable use of GAI
• Rethink your assignments & assessments
• Consider alternate subject formats
• Design for equity, accessibility, and student privacy

For a variety of AI-aware assessment makeovers see our AI-Aware Implementation: Examples page .

The corresponding tabs below are designed to guide you through your planning process and support your teaching throughout the semester.

In addition, you can download our Quickstart Guide to Thinking about AI in your Course to reference when considering how to use (or not use) genAI in your course.

Are you interested in leveraging the utility of generative AI to create more meaningful assignments and more authentic learning experiences? Would you like to talk with someone about your ideas and concerns about genAI? Contact us (TLL@mit.edu) to request a consultation.

Over the years, our ideas have changed regarding what students need to know how to do “from scratch” and what they can outsource to technology. For example, many courses permit (encourage) the use of software packages for mathematical operations and/or the analysis of scientific and engineering processes (e.g., Wolfram Alpha, Matlab, etc.) and the use of existing pieces of code (e.g., GitHub, etc.) in programming. And, of course – there is the calculator.

Before considering the affordances or annoyances of generative AI in your teaching context, it is important to critically examine your real goals for student learning . Are there levels of higher-order thinking – more complex, more authentic learning goals – that you’d like students to achieve? If so, you can begin to explore how generative AI tools help students achieve those goals.

In particular, you may wish to:

• Engage with GenAI and examine how it handles your current assignment prompts and problems. Think back to your ideal goals for student learning and consider how you can modify your assignments to support your actual goals for student learning.
• Consider how you might (re)design your assignments and/or course format to leverage generative AI and better support meaningful student learning.
• Consider how GenAI can help you leverage the science of learning in your teaching. See TLL’s post, Applying the Science of Learning in your Teaching: Generative AI May Help .

Teaching + Learning Lab Staff are available to help you refine your goals. Contact us at tll@mit.edu.

Note that OpenAI’s terms of use include the following among its restrictions: users may not “represent that output from the Services was human-generated when it is not.” For guidelines on properly attributing GenAI output, see: How to Cite ChatGPT .

Teaching + Learning Lab Staff are available to help you develop an AI Acceptable Use statement for your syllabus. Additional resources are provided below.

Writing in his blog, Agile Learning , Derek Bruff suggests that instructors ask themselves the following questions when considering an assignment revision:

• Why does this assignment make sense for this course?
• What are specific learning objectives for this assignment?
• How might students use AI tools while working on this assignment?
• How might AI undercut the goals of this assignment? How could you mitigate this?
• How might AI enhance the assignment? Where would students need help figuring that out?
• Focus on the process. How could you make the assignment more meaningful for students or support them more in the work?

Helping students to engage with the learning process may be particularly relevant here at MIT, where developing students as agile critical thinkers and problem solvers are primary and essential goals of an MIT education and cornerstones of the campus ethos. Experts in a field are fluent in the problem-solving process. They are comfortable “playing” with multiple solution paths and ideas – i.e., hitting dead ends – and learning from these mistakes to eventually formulate solutions. Many novices (our students included) believe if they don’t see the solution right away, that they will fail. Learning how to solve problems involves learning from failed solution attempts and accepting that initial “failure” is almost always part of developing a successful solution. Here, a focus on the process , in addition to the product, can help students achieve our goals for them as MIT graduates and minimize chatbot plagiarism. (For more on expert v. novice learners , see the Resources section below.)

Higher education author and consultant John Warner recently commented: “ One of the hallmarks of growing sophistication as a writer is seeing the idea you thought you were expressing change in front of your eyes as you are writing. This is high-level critical thinking. This kind of emergent rethinking is an experience that every college-level writer should be familiar with. ” (Warner, 2022).

And, as Nancy Gleason, director of the Hilary Ballon Center for Teaching and Learning at NYU Abu Dhabi, wrote in The Times Higher Ed , “[…the assessment of only] a completed product is no longer viable. Scaffolding [and assessing] the skills and competencies associated with writing, producing and creating is the way forward.” (Gleason, 2022).

Consider the usefully prescient comments of cognitive and learning scientist Michelene Chi in her 1994 paper on the impact of self-explanations during problem solving on the improvement of student science understanding: “ …especially for challenging science domains….students should learn to be able to talk science (to understand how the discourse of the field is organized, how viewpoints are presented, and what counts as arguments and support for these arguments), so that students can participate in scientific discussions, rather than just hear science. ” (Chi, 1994)”

In subjects that use problem sets , ask students to explain their thought processes as they solve (a subset of) the problems.

A few (of many possible) helpful prompts may include asking them to describe:

• Why they chose a particular method;
• Why they made certain assumptions and/or simplifications;
• Where they ran into dead ends and how they found their way forward;
• What broader takeaways did they learn from solving the problem?

As a follow-up to any type of assignment, you can ask them to reflect on and articulate:

Regardless of your assignment types (psets, weekly papers, reading reflections, coding), consider adding weekly, low-stakes, in-class quizzes that ask them to solve a close variant of one or two of the pset questions from the week and/or a previous week; reflect on and/or summarize a discussion from a previous class.

Frequent, low-stakes quizzing allows students to practice retrieval – a key component of the learning process, and if the quizzes pull questions from previous weeks, it can further develop students’ ability to retrieve and apply recently learned concepts and skills. See TLL’s How to Teach pages for additional information on retrieval and spaced & interleaved practices.

If you use this strategy, you’ll want to:

• Be explicit with students about what you are doing and why.
• Include follow-up questions from the previous section of the course (above) in your pset – to encourage students to reflect on the problem-solving process.
• During the first few weeks of the semester, model the type of reflective and engaged responses that you are looking for on the quizzes.
• Reconsider how you allocate points for the various assignments in your subject. With the addition of weekly, in-class quizzes – you may want to decrease point value and/or the grading time required for other assignments.

As noted in the Refine Your Goals for Learning section, the affordances of GenAI may allow you to better support students’ development of higher-order, more complex skills like synthesis, analysis, and creation.

In their paper, Mollick and Mollick offer detailed descriptions of ways to leverage programs like ChatGPT in student assignments. They suggest that “ …AI can be used to overcome three barriers to learning in the classroom: improving transfer , breaking the illusion of explanatory depth , and training students to critically evaluate explanations .” (Mollick & Mollick, 2022).

In line with the Mollicks, Lucinda McKnight, senior lecturer in pedagogy and curriculum at Deakin University, offers several suggestions for incorporating genAI into student assignments to support deeper and more robust learning, including:

Many of the resources included at the end of this section provide examples of redesigned assignments from a wide range of disciplines that leverage or mitigate the use of GAI.

Staff from the Teaching + Learning Lab are available to help you revise your assignments and assessments.

• Hardiman, P.T., Dufresne, R. & Mestre, J.P. (1989). The relation between problem categorization and problem solving among experts and novices . Memory & Cognition 17, 627–638. https://doi.org/10.3758/BF0319708 5
• Larkin, J., McDermott, J., Simon, D.P., & Simon, H. (1980). Expert and Novice Performance in Solving Physics Problems. Science , 208(4450). pp. 1335-1342. DOI:10.1126/science.208.4450.1335
• Polya, G., & Conway, J. H. (2014). How to solve it: A New aspect of mathematical method . Princeton University Press.
• Wankat, P.C., and F.S. Oreovicz (2015). Problem solving & creativity in Teaching Engineering , Second Edition (pp. 93-115). Purdue University Press. (Open Access Edition)

For information on developing psets that incorporate GenAI, see our post: Rethinking Your Problem Sets in the World of Generative AI

• Mollick, Ethan R. and Mollick, Lilach, Assigning AI: Seven Approaches for Students, with Prompts (June 12, 2023). Available at SSRN: https://ssrn.com/abstract=4475995 or http://dx.doi.org/10.2139/ssrn.4475995
• Mollick, Ethan R. and Mollick, Lilach, New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments (December 13, 2022). Available at SSRN: https://ssrn.com/abstract=4300783 or http://dx.doi.org/10.2139/ssrn.4300783

For additional ideas for rethinking assignments, see Appendices B-F of Cornell’s Committee Report: Generative Artificial Intelligence for Education and Pedagogy

• Appendix BAppendix B Courses that develop writing as a skill
• Appendix CAppendix C Creative courses for music, literature, and art
• Appendix DAppendix D Courses in social sciences
• Appendix EAppendix E Mathematics, physical sciences, and engineering
• Appendix FAppendix F Courses in programming

If you are committed to limiting students’ use of genAI in your subject, you may want consider how a change in its format and/or structure of might impact students’ use of genAI. Take a fresh look at Blended Learning (BL). In BL, students generally view recordings of lectures and/or engage with pre-class readings to gain a basic understanding of relevant topics (this is the “information-delivery” component of the class). They then engage in active problem solving (information retrieval and application and knowledge creation) during class time. Depending on the subject (topic, level, etc.), you may be able to restrict students’ use of computers during class to ensure that they are engaging in traditional problem solving as they grapple with key problems from the week’s material. 1 Additionally, even if students are required to use particular software or applications – you can monitor their use during class time.

Teaching + Learning Lab staff are available to help you implement blended learning in your courses. Additional Resources are provided below.

• Professors Wolfgang Ketterle and Lorna Gibson describe their uses of flipped classrooms (blended learning) to better support learning and engage students.
• The Blended Learning section of the Report of the RIC16 Ad Hoc Committee: Leveraging Best Practices from Remote Teaching for On-Campus Education
• Dealing with the Lack of Student Engagement in Lectures . Richard de Neufville, MIT Faculty Newsletter , January-April 2023, vol. XXXV no. 3
• Blended Learning . Center for Teaching & Learning at Columbia
• Freeman S, Haak D, Wenderoth MP. Increased course structure improves performance in introductory biology. CBE Life Sciences Education 2011 Summer;10(2):175-86. doi: 10.1187/cbe.10-08-0105. PMID: 21633066; PMCID: PMC3105924. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3105924/

1 If you use this approach – make sure that you can accommodate students who require the use of computers, and/or specific software, etc. Reach out to Disability and Access Services (DAS) for guidance.

Currently, OpenAI’s ChatGPT-3.5 is open-access and free but more prone to producing incorrect (“hallucinated”) responses. ChatGPT-4 is much “better” than ChatGPT-3.5 but costs $20/month. Bing, Microsoft’s version, has comparable capability to ChatGPT-4 and is “free” but requires a Microsoft account and must be used in the Microsoft Edge browser. Without some planning on your part, some students in your class will have access to ChatGPT-4, while others may only have ChatGPT-3.5 or Bing. See the Assessments & Assignments section for ideas for more equitable incorporation of GenAI in your subjects.

Concerning accessibility, writing in Wired, Pia Ceres writes, “completely barring ChatGPT from classrooms, tempting as that may be, could introduce a host of new problems. Torrey Trust at the University of Massachusetts Amherst studies how teachers use technology to reshape learning. She points out that reverting to analog forms of assessment, like oral exams, can put students with disabilities at a disadvantage.” (Ceres, 2023)

See the Transparency in Learning and Teaching project for additional resources for developing transparent and equitable assignments and assessments or contact MIT’s Disability and Access Services (DAS) .

If you would like students to engage with AI-generated content in your subjects, consider student privacy issues (e.g., ChatGPT is an open-access tool, not supported by IS&T and not subject to MIT’s student data safeguards), as well as the ethics of mandating that students use the tool. Read and encourage all students to read ChatGPT’s privacy policy, which states that data collected by ChatGPT can be shared with third-party vendors, law enforcement, affiliates, and other users, and the terms of use, which states that “you must be 18 years or older and able to form a binding contract with OpenAI to use the Services” (i.e., students under 18 years old should not be asked to use the tool.) Users can request to delete their ChatGPT account, but all prompts and inputs to the site cannot be removed. Writing in her blog, Jill Walker Rettberg, professor of digital culture at the University of Bergen in Norway, notes, “OpenAI knows my email and the country I am connecting from, so they can assume my judgements [sic] about how ChatGPT responds to me align with “Norwegian values.” OpenAI also knows what device, browser and operating system I am using, which can be a proxy for class and socio-economic status.” (Rettberg, 2022)

To address data privacy concerns, consider ways that students can use AI-generated content without generating it themselves (E.g., you or a TA volunteer could enter questions/prompts as specified by students and share them for use in the assignment).

For additional guidance re student privacy and GAI use, see MIT Sloan’s Generative AI for Teaching & Learning Resources Hub

Teaching + Learning Lab Staff are available to help you address these concerns.

Staff from TLL and Open Learning-Residential are available to facilitate discussions within MIT DLCs on the topic of Teaching with GenAI . MIT community members can email ai-teaching@mit.edu to arrange a session. If you’d like to facilitate your own discussion – a customizable slide deck is available here . [You will be prompted to make a copy.]

Sloan – Teaching & Learning with Generative AI Hub

Advice Concerning the Increase in AI-Assisted Writin g , Profs. Ed Schiappa and Nicholas Montfort, Comparative Media Studies & Writing @ MIT.

If you are an MIT community member and have resources you’d like to share, please email us at: ai-teaching@mit.edu.

1 The Poorvu Center for Teaching and Learning at Yale states: “Reflective teaching involves examining one’s underlying beliefs about teaching and learning and one’s alignment with actual classroom practice before, during, and after a course is taught. When teaching reflectively, instructors think critically about their teaching and look for evidence of effective teaching.”

2 For a good overview of what LLMs are and how they work, see. Kevin Rose and Cade Metz, On Tech: AI Newsletter series: How to Become an Expert on A.I. , New York Times , 7 April 2023.

• GAI Basics: a Technical Primer – MT Sloan , Rama Ramakrishnan, MIT Sloan (Video); part of Sloan Technology Services AI Hub
• An MIT Exploration of Generative AI
• Teaching + Learning Lab GAI Blog posts
• Open AI’s ChatGPT documentation

• AI Meets Education at Stanford (AIMES)
• Generative AI in Teaching & Learning – Teaching Hub, University of Virginia
• OSU Faculty Recommendations
• Philippa Hardman’s AI Learning Taxonomy
• How should AI-generated content be labeled? MIT Sloan
• There are some incredible folks writing about this on Substack . We recommend: Marc Watkins – Rhetorica Beyond ChatGPT Series: Reading: No One is Talking About AI’s Impact on Reading AI’s Promise to Pay Attention for You Teaching is Not a Problem for AI to Solve Ethan Mollick – One Useful Thing All of Mollick’s posts interesting and thought provoking Ethan & Lillach Mollick have a very useful paper called: Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts (note that this article addresses teaching practices (rather than student work)) Ethan Mollick’s examples of elaborate prompts in Assigning AI: 7 Ways of using AI in Class Ai x Education Josh Brake – The Absent Minded Professor Lance Eaton – AI + Education = Simplified Eric Hudson – Teaching on Purpose Nick Potkalitsky – Educating AI Emily Pitts Donahoe – Unmaking the Grade See additional authors/Substacks in the sections below
• OpenAI Teaching w/ChatGPT Handbook
• MIT Physics YouTube Playlist: Impact of ChatGPT
• Applying the Science of Learning in Your Teaching: Generative AI May Help , MIT TLL Blog.
• Acceptable GAI-use: Syllabus Statements: KTCP syllabus statement Lance Eaton: Crowd-sourced Google Doc (caveat emptor: some statements are from the dawn of GPTs) Derek Bruff: Show us your syllabus – Chatbot edition The Best AI Policies I’ve Seen So Far Daniel Stanford’s Substack Other syllabus statements from TLL

• Laura Roberts’ AI Tool Box for Research & Writing , Padlet
• An Introduction to Teaching with Text Generation Technologies , Tim Laquintano, Carly Schnitzler, and Annette Vee. Writing Across the Curriculum at Colorado State.
• Annette Vee, AI Aware Teaching Examples , AI & How We Teach Writing
• Four Rules for Writing in the Age of AI , writinghacks.substack.com
• Jane Rosenzweig: Writing Hacks (Harvard College Writing Center)
• Josh Brake The Absent Minded Professor https://joshbrake.substack.com/p/process-over-product “But writing is not just about the text that you write at the end of it. Writing is about the process that leads you to that eventual end product. It’s about iteration and failure: the cycle of drafting, critiquing, editing, and revising. It’s the same underlying truth about any worthwhile pursuit: it’s about the journey and not just the destination.”
• Lauren Goodlad and the team at Critical AI : Jane Rosenzweig: https://criticalai.org/2023/09/21/teaching-insights-what-happens-when-a-novice-writer-asks-chatgpt-for-editing-advice-dr-jane-rosenzweig/
• AI Pedagogy Project – Harvard
• Perry Share Welcome to the machine: Generative AI & higher education: a selective guide to resources (see Student Writing)
• Anna Mills Template Phrases for Critiquing AI Outputs
• Leon Furze – A series of blog posts that explore how multimodal generative AI (genAI) writing tools can be applied to all stages of the writing cycle, from purpose through to publication. Purpose Exploration Ideas Skills Collaboration Publication
• Cornell: https://teaching.cornell.edu/generative-artificial-intelligence/cu-committee-report-generative-artificial-intelligence-2
• https://docs.google.com/document/d/1uyrUHFfTuGsi-c3xtIOy3wNIsw29UD2F1DqttOiFJuI/edit
• How to Cite ChatGPT , APA.

• Derek Bruff, Agile Learning Blog Assignment Makeovers in the AI Age: Reading Response Edition , Assignment Makeovers in the AI Age: Essay Edition
• Integrating AI into Assignments to Support Student Learning , UVA Teaching Hub
• Marc Watkins, CHE – Make AI Part of the Assignment : Include an AI-assisted Learning Template as part of student assignments
• Rethinking Your Problem Sets in the World of Generative AI , MIT TLL
• Reimagining Assessments in the Age of AI , aixeducation.substack.com:
• Assessment for Reform in the Age of Artificial Intelligence , TESQA – Australia
• Appendices B-F of Cornell’s Committee Report: Generative Artificial Intelligence for Education and Pedagogy Appendix B: Courses that develop writing as a skill Appendix C: Creative courses for music, literature, and art Appendix D: Courses in social sciences Appendix E: Mathematics, physical sciences, and engineering Appendix F: Courses in programming
• Filling in Research Gaps with Generative AI . Carrick, T.H. (2023). John S. Knight Institute for Writing in the Disciplines
• Assigning AI: Seven Approaches for Students, with Prompts , Mollick, Ethan R. and Mollick, Lilach, (June 12, 2023).
• New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments (December 13, 2022).
• Daniel Stanford. Incorporating AI in Teaching: Practical Examples for Busy Instructors
• Brett Becker. Programming Is Hard – Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation
• Ryan Cordell. Building a (better) book) – Course taught at UIUC: Lab 1: Amanuenses to AI

• Coursera Course: Prompt Engineering for ChatGPT
• Ethan Mollick’s examples of elaborate prompts in Assigning AI: 7 Ways of using AI in Class
• Open AI’s documentation on prompt engineering

• Researchers reduce bias in AI models while preserving or improving accuracy | MIT News, December, 2024
• How Harmful Are AI’s Biases on Diverse Student Populations? | Stanford HAI, October 2024
• Generative AI bias poses risk to democratic values, research suggests , Science X, February 2025
• Lauren Leffer, Humans Absorb Bias from AI—And Keep It after They Stop Using the Algorithm , Scientific American
• Better images of AI A site to combat bias in AI generated images
• Navigating The Biases In LLM Generative AI: A Guide To Responsible Implementation
• Broader implications and critical perspectives: Section 6, p. 24 of Perry Share’s compilation )
• Humans are Biased Generative AI is Even Worse , Bloomberg, 2023.

• Generative AI and Policy Development: Guidance from the MLA-CCCC Joint Task Force on Writing and AI , April 2024.
• Anna Mills, Why I’m using AI detection after all, alongside many other strategies , February 2025.
• Christopher Ostro A big picture look at AI detection tools , Teaching in Higher Ed podcast Slides Video
• Laura Dumin, AI detectors: A look at some arguments and where I ended up , February 2025.
• Derek Newton – The Cheat Sheet ,academic honesty blogger with many posts on the use of AI detection software.
• University of Sydney What to do about assessments if we can’t out-design or out-run AI? , Danny Liu and Adam Bridgeman, 2023 Follow-up FAQ , 2025
• Joseph Thibault, Agentic AI & Academic Integrity .
• AI Detectors Don’t Work. Here’s What to Do Instead , MIT Sloan Teaching & Learning Technologies,

Chi, M. T., De Leeuw, N., Chiu, M., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science , 18(3), 439-477. https://www.sciencedirect.com/science/article/pii/0364021394900167

Gleason, Nancy (2022). ChatGPT and the rise of AI writers: How should higher education respond? Times Higher Education .

McKnight, Lucinda (2022). Eight ways to engage with AI writers in higher education. Times Higher Education .

Mollick, E., & Mollick, L. (2022). New modes of learning enabled by AI chatbots: Three methods and assignments. Social Science Research Network . https://doi.org/10.2139/ssrn.4300783

Warner, J. (2022, August 31). The biggest mistake I see college freshmen make. Slate Magazine . https://slate.com/human-interest/2022/08/advice-to-first-year-college-students-on-freshman-comp.html

• Thanks to Dr. Rachel Remmel at the University of Rochester TLC for passing along many of these resources ↩︎