One of the most frequent requests I get from faculty is to see examples of actual assignments that thoughtfully integrate generative AI. I am very happy to share a new collection of such assignments on the University of Virginia Teaching Hub: "Integrating AI into Assignments to Support Student Learning."

In my day job at the University of Virginia, I'm helping to support about 50 faculty fellows who are part of UVA's Faculty AI Guides program. These faculty are exploring the use of generative AI in their own teaching and serving as resources for colleagues in their departments and schools. The goal of the program is to help all UVA faculty make intentional and informed choices about AI policies for their courses. Given that these choices are often discipline-specific, I think UVA's approach to faculty development around AI makes a lot of sense.

At our all-Guides meeting in January, we took a little time to invite the Guides to share examples of teaching materials they have used in their experiments with AI. We collected a range of syllabus statements, assignments, and class activities. Not all were shared with permission to share beyond UVA, but some were. The collection mentioned above features sample AI-integrated assignments from four of the Guides, and you can read sample AI syllabus statements shared by four other Guides on the Teaching Hub, as well.

As I've been sharing and discussing the sample AI assignments with colleagues at UVA and elsewhere, I've found myself putting the assignments into three categories:

Category 1: Green Light Assignments. Kiera Allison asks students in her management communication course to take on a persuasive task that feels impossible and explore how AI might help them accomplish this task. Her "Do Something Impossible with AI" assignment gives students a green light to explore any and all ways AI might be useful in this project and, importantly, asks students to document and reflect on their use of AI. Kiera is looking for creative uses of AI and thoughtful reflections on what an effective human-AI collaboration might look like. The documentation and reflection is key to an assignment like this, since it creates opportunities for critical thinking and open dialogue about the role of AI in learning. Jun Wang's assignment in the collection is another example of a green light assignment.

Category 2: Red Light, then Green Light Assignments. In his "Finding Your Voice in French" course, Spyros Simotas asks students not to use AI for the first two parts of a writing assignment. For the last two parts, students are encouraged to use AI but with a critical lens. This red light, then green light structure is a popular one, sometimes applied at the assignment level as in Spyros' case, sometimes applied at the course level, with AI use forbidden until the second half of the course. One aspect of Spyros' assignment description I really like that it includes a suggested AI prompt for students to use in the second half of the assignment, with a breakdown of the prompt to help students understand how to better prompt AI chatbots. For another example of a red-then-green assignment, see this "human vs. AI" assignment from past podcast guest Pary Fassihi.

Category 3: Human Critique the Chatbot Assignments. In one of the assignments in Jamie Jirout's course on cognitive psychology and education, she asks students to have their AI chatbot of choice generate a mini case study on a particular course topic, then critique the generated case study. Then students are asked to revise the AI-generated case study based on their critique. This is another common assignment structure for exploring AI within a specific field, one that I highlighted here in the newsletter almost two years ago. One choice Jamie has made that I appreciate is to give students specific criteria and associated questions for their critique of the AI output.

As I write this, I'm looking over some other assignments that Pary Fassihi shared when she came on the podcast, and some of them don't fit the above categories. So here are two other categories of AI-integrated assignments:

Category 4: Chatbot Critique the Human Assignments. We've probably heard of assignments where students do some writing and ask ChatGPT or some other AI tool for feedback. What I like about Pary's "peer review" assignment, in which ChatGPT substitutes for a student peer reviewer, is that she provides students with a set of very specific criteria to share with the chatbot to shape its review. "Evaluate the evidence used to support the main argument. Is the evidence relevant, sufficient, and effectively integrated into the argument?" or ""Assess the organization of the paper. Is the argument presented in a logical, coherent manner that is easy to follow?" These prompts mean the AI provides more useful feedback and help tie that feedback to specific assignment objectives.

Category 5: Co-Creation Assignments. I'm naming this category based on an assignment that I don't have permission to share, at least yet, from another UVA colleague. That assignment asks students to co-create a specific kind of poem with an AI chatbot. Pary's assignment in this category is "AI-Inspired Art Creation: A Critical Exploration of AI, Arts, and Ethics." Her assignment asks students to create images inspired by specific artists using AI tools like DALL-E (now integrated in ChatGPT) and Adobe Firefly. Students then reflect on the experience, discussing the ethics of using AI to mimic historical artists and more generally trying to answer the question "Is this art?" As with green-light assignments, this type of assignment asks students to document and reflect on their AI use, but there's more direction in how students should use the AI in the specific creative process at hand.

This isn't an exhaustive list of categories, of course! I'm still trying to figure out how to category some of Jamie Jirout's other assignments, which involve targeted explorations of AI tightly integrated with course learning objectives. But this list is a good place to start, I think, when considering how to integrate AI thoughtfully in your own assignments.

Thanks to all the faculty mentioned here for sharing their AI assignments so that we can learn from them!

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