Generative AI & Your Course

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.

Resources

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.