Generative AI & Your Course

Read, Refine, Define & Articulate

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 reflect1 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 generative AI 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 generative AI, 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

The corresponding tabs below are designed to guide you through your planning process and support your teaching throughout the semester. We hope you find this page useful and informative as you prepare for the fall and throughout the academic year.

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 ( to request a consultation.

Refine Your GoalsDefine Acceptable UseRevise Your Assignments & AssessmentsConsider Course FormatEquity, Access, Privacy

How might generative AI prompt us to reconsider and refine goals for student learning?

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

AI Acceptable Use Statements

Regardless of your thoughts on using GenAI in your subject, convey those thoughts, the resultant subject policies, and the consequences of their violation with your students at the beginning of the semester. Including an AI Acceptable-use Statement in your syllabus and discussing your rationale for the policy on the first day of class can help regulate students’ use of GenAI and open the door for additional conversations about GenAI and learning in your subject as the semester progresses. If you choose to permit using GenAI for assignments, clearly specify how you expect students to cite and document its use.

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.

As you rethink your assignments and assessments in light of genAI, keep your revised goals for student learning in mind. Consider whether you are hoping to leverage genAI’s affordances or mitigate its use.

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?

Mitigating the Use of GenAI

For many instructors, thinking about the process of student learning and the assessment of that process is a useful way to (1) help students develop the habits of mind and skills essential to the discipline (or subject) and (2) shift the focus of student learning assessment away from end products that may lend themselves to genAI reliance and plagiarism.

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:

  • Any issues they had in beginning the assignment;
  • What they found most interesting or surprising;
  • Their “aha moment”;
  • How the completion of the assignment added to their understanding of the topic, etc.

Developing students’ metacognitive skills by requiring them to self-regulate and self-explain their solution process may mitigate their use of AI-generated responses. Self-evidently, it is much more difficult for students to explain their problem-solving process when they didn’t actually solve the problem! If a student uses generative AI in some aspect of the solution, the requirement that they document their thought processes will force them to engage a bit deeper with certain aspects of the problem and the learning process overall.

Frequent Low-stakes, In-class Quizzes

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.

Leveraging GenAI

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:

  • Use AI writers as researchers. They can research a topic exhaustively in seconds and compile text for review, along with (real & hallucinated) references for students to follow up. This material can then inform original and carefully referenced student writing.
  • Use AI writers to produce text on a given topic for critique. Design assessment tasks that involve this efficient use of AI writers, then [ask students to provide] critical annotation of the text that is produced.
  • Use different AI writers to produce different versions of text on the same topic to compare and evaluate.
  • Use and attribute AI writers for routine text, for example, blog content. Use discrimination to work out where and why AI text, human text, or hybrid text are appropriate and give accounts of this thinking.
  • Research and establish the specific affordances of AI-based content generators for your discipline. For example, how might it be useful to be able to produce text in multiple languages in seconds? Or create text optimized for search engines?
  • Explore different ways AI writers and their input can be acknowledged and attributed ethically and appropriately in your discipline. Model effective note-making and record-keeping. Use formative assessment that explicitly involves discussion of the role of AI in given tasks. Discuss how AI could lead to various forms of plagiarism and how to avoid this. (McKnight, 2022).

Whether or not you explicitly incorporate generative AI into all your assignments (or a subset of your assignments), make sure to stress that it doesn’t always produce correct answers and provide examples. Underscore that genAI output requires reflection and input from humans.

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.


Expert v. novice learners


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

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

Course Format

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.

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).

Student Privacy

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.

MIT Resources and Support

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 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

LLMs in Education at MIT: A Discussion among faculty and instructors organized by Prof. Craig Carter (DMSE), IAP 2024.  Details coming soon.

Mary Fuller’s guide for students: GAI for Writing.

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

If you are an MIT community member and have resources you’d like to share, please email us at:

Additional Resources on the Use of Generative AI in Teaching & Learning

TLL’s blog posts:


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.


Chi, M. T., De Leeuw, N., Chiu, M., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439-477.

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.

Warner, J. (2022, August 31). The biggest mistake I see college freshmen make. Slate Magazine.