Conceptual Understanding, Technical Skills, and Generative AI Literacy

One of the perks of working at the Center for Teaching Excellence at the University of Virginia this year is getting to meet UVA faculty who are doing really interesting work in their teaching. Back in August, I had the chance to spend a couple of days on Grounds, as they say there, working with UVA’s Faculty AI Guides. These are faculty fellows who are experimenting with the use of generative AI in their own teaching and serving as a resources for colleagues in their departments and programs who have questions about generative AI.

During that Faculty AI Guides institute, I met Jingjing Li, Andersen Alumni associate professor of commerce. Jingjing teaches business intelligence at both the undergraduate and Master’s levels, and her research interests include artificial intelligence and data analytics. When she described her thoughtful use of generative AI in her courses (during the institute), I knew I wanted to invite her on the podcast! I’m excited to share that interview on the podcast this week.

In the interview, Jingjing describes a major assignment in her courses, one that asks students to code and tune a machine learning model for some kind of business intelligence task. For example, students might be trying to predict whether a customer is likely to return based on a small set of data about that customer. Jingjing is quick to point out that this isn't just a machine learning task, that there's a business decision that follows the prediction. If the model predicts a customer won't return, should the business invest resources in trying to bring that customer back?

Although Jingjing is teaching about one branch of artificial intelligence (machine learning), she's using a different branch of AI (generative AI) to support that teaching. In the interview, she describes asking students to work the project on their own first and then refine their work with the help of ChatGPT. The students turned in all their work, including their chat logs, as well as a reflective statement about working with AI on this project. They were also asked to come up with a metaphor that captures their experience of working with AI.

All of Jingjing's findings are fascinating! She found interesting differences in the kinds of metaphors students create, differences that reflect the students' success in using generative AI to enhance their projects. She also found some interesting relationships between a student's conceptual understanding of the topic (machine learning use in business intelligence), skill at writing computer code (which is used to implement the machine learning model), and generative AI literacy (that is, their ability to get ChatGPT to do useful things).

​You can listen to my interview with Jingjing Li here, or you can search "Intentional Teaching" in your favorite podcast app. And Intentional Teaching Patreon supporters can listen to a short bonus episode in which Jingjing provides a brief history of artificial intelligence.

Strategies for Teaching AI Ethics

I'm leading two faculty learning communities at UVA this fall that are reading Teaching with AI: A Practical Guide to a New Era of Human Learning by Jose Antonio Bowen and C. Edward Watson. We finished the book, and this morning I was planning the final meeting of the semester. While the book works well as a syllabus of sorts of this kind of reading group, it is not without flaws. One of those flaws is that it largely avoids critical engagement with the many challenging ethical questions about generative AI.

I'm not planning to try to resolve those ethical questions in the remaining 60-minute session of these faculty learning communities, but I am considering spending some of that time exploring ways to teach students about these ethical questions. In just a bit of searching, I found three interesting articles on this topic. I thought I would share them here in the newsletter and invite readers to point me to other examples of ways to teach AI ethics. I will happily share useful examples submitted by readers in a future newsletter!

• ​Teaching Social Identity and Cultural Bias Using AI Text Generation - This is a contribution by Christopher Jimenez to the Writing Across the Curriculum (WAC) Clearinghouse. The piece describes a course activity that revolves around providing an AI chatbot some information about a person (real or fictional) that doesn't on its surface have to do with the person's social or cultural identities, then asking the chatbot to predict the person's social or cultural identities. This led to a productive discussion about the performance of identity and bias in generative AI.
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• ​Professor Bot: An Exercise in Algorithmic Accountability - This is another piece I found in the WAC Clearinghouse. In this article, Jentery Sayers describes an assignment that helps students explore the role and potential concerns of involving AI in decision-making processes. The assignment uses a near-future hypothetical scenario where AI tools are used to evaluate college admission essays. The assignment was developed before the release of ChatGPT, and it doesn't actually involve the use of AI. Instead, it works as a thought experiment to help students identify the ethical issues involved and point them to regulation and policy questions.
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• ​Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics - This article by Kristin Bartlett and Jorge Camba doesn't provide an activity plan like the other two resources on this list, but it does offer a detailed and nuanced account of the ethical issues of using generative AI, especially image generators, in product design. They tackle topics including bias, originality, intellectual property, and hidden labor costs. They also outline course policy options for a product design course that are informed by these ethical concerns.

Have you had success in teaching students about AI ethics? If so, what kinds of activities or assignments have you used?

Thanks for reading!

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