Teaching & Learning in the Age of AI

At the Sherrerd Center, we are sustained by the conviction that if instructors are informed by the principles of the learning sciences, committed to intentional and transparent teaching practices, and clear-eyed about the challenges and opportunities posed by the expansion of generative AI, then these new technologies and the ones that follow pose no fundamental threat to liberal arts teaching and learning. Rather, we move forward confident that the liberal arts provides us with a road map to navigate these societal challenges and is thus more essential than ever. The Sherrerd Center exists to provide the programs, policy guidelines, and teaching resources to turn these principles into practical classroom reality.

Approaching AI

The rise of generative AI poses a range of pressing challenges for higher education. It also offers a profound opportunity to reconsider the capacities our students need for today’s world, to sharpen our pedagogical strategies, and to strengthen our investment in deep learning. Guided by principles from the learning sciences, the Sherrerd Center for Teaching and Learning supports instructors from across all disciplines as they wrestle with the complexities of this moment. We guide instructors as they explore and adopt a range of approaches—from critical interrogation to bold experimentation and everything in between. In the spirit of the liberal arts, we embrace the frictions between divergent perspectives and approaches as an indispensable opportunity to learn and practice across differences. And we imagine our teaching spaces as sites where we are not just preparing for the future but also actively and intentionally shaping it. With support through this pivotal time, faculty will continue to embolden students who are driven by curiosity, committed to ethical engagement, confident in their ability to navigate uncertainty, and enriched by the enduring values of a liberal arts education.

For more on developing AI policies in your courses and pedagogy more broadly, visit our Resources page.

Pedagogical Partnership

In Fall 2026, the Sherrerd Center is offering a special edition of our Pedagogical Partnership program focused on generative AI and the role it plays in our classrooms.

This is a great opportunity to harness student feedback and partnership to meaningfully address some of the challenges we’re experiencing in our classrooms and to think deeply about one of the most pressing teaching issues of our time.

Learn More

Faculty Stories

Cati Bestard

Visiting Assistant Professor of Art

“Photography has always evolved alongside technological advances. Text-to-image generative technology is no exception, impacting the process of image-making and the way we look at images. With this in mind, I prompted my advanced photo students to create a project using these tools.

Not all students were willing to engage, but even their resistance found expression in the work. The projects ranged widely: restoring color to damaged Chinese cave paintings, imagining fictional representations of Asian Americans in influencer-designed worlds as a critique of photography’s diversity gap, and questioning how AI-generated images erase the specific place and time of making, diminishing the value of that lived experience.

Overall, the conversation was productive and dynamic. Most students valued having an open space to discuss the pros and cons, wrestle with ethical and environmental implications, and sit with the sense of inevitability that surrounds these tools.”

Glenn Ellis

Gates Foundation Professor of Engineering; Director of the Picker Engineering Program

“In EGR 389: Techniques for Modeling Engineering Processes, I run a two-week jigsaw activity where generative AI is integrated as an epistemic agent—a cognitive sparring partner that challenges how students think, rather than writing answers for them. This approach drives Carl Bereiter’s principles of Knowledge Transforming.

The activity progresses through three collaborative phases:

Phase 1 (Expert Groups): Students form teams based on interest to deep-dive into an ethical issue (e.g., AI bias, ownership of ideas, machine consciousness) and collaboratively co-create a concept map. Students prompt the AI to unpack complex material and critique their ideas.

Phase 2 (Think Tank Jigsaw): Students reshuffle into peer-led "think tank" groups containing one expert from each topic. Class time is flipped as they teach one another their respective areas.

Phase 3 (The Policy Critique): Using their combined peer-taught knowledge, each think tank collaborates to critique the White House AI Action Plan, using the AI to stress-test their policy logic.

Throughout, I act as an epistemic adviser, guiding students as they manage their AI dialogues and helping them take ownership of their intellectual growth.”

Christophe Golé

Professor of Mathematical Sciences

“This past academic year, I incorporated AI-admissible assignments into three of my math classes. In Multivariate Calculus (MTH 212), students studied the graph of a complicated multivariate function—a problem we explicitly framed as impossible to do by hand, encouraging them to use technology. Some used AI to learn how to code the appropriate numerical tools, while others asked AI to solve the problem directly.

In Discrete Math (MTH 153), we devoted a class session to exploring number theory questions on the computer that would be inaccessible by hand. This was followed by a project in which students used AI to help develop the code needed to answer further questions and follow-up questions we required that they come up with.

Finally, in Advanced Calculus (MTH 280), students posed questions about course material—mostly ‘manifolds’—that went beyond what we could cover in class, allowing them to explore aspects of modern math they otherwise wouldn't encounter before graduate school.

All three assignments were preceded by class discussions and included by a meta-reflective piece in which students considered how they felt about AI, both as users and as citizens of the world. I was encouraged by the thoughtfulness of their responses, and they seemed to find the experience valuable overall.”

Lu Yu

Senior Lecturer in East Asian Languages & Literatures

“I experimented with AI tools in an advanced Chinese language course by integrating ChatGPT, DeepSeek, and 文心一言(ERNIE, developed by a Chinese company) into daily classroom activities. One goal was to improve students’ language skills. For example, I asked students to ask a question in Chinese and summarize what they learned from the AI generated responses. However, it didn’t work well because the language AI produced was beyond students’ level. Also, many students had negative attitudes towards AI tools and were reluctant to use them in class.

Another goal is to help students understand the differences among AI tools and the cultural and social contexts behind them. For example, 文心一言 was not able to answer questions related to Taiwanese identity issues, because China-Taiwan relation is a sensitive issue and censored in China. ChatGPT had difficulty creating appropriate images of Chinese dishes. Students showed strong interest in discussing these differences and the reasons behind them.

Overall, students had negative attitudes about AI tools. In the following semester, I continued experimenting with AI in an intermediate-to-advanced Chinese language class. I showed students how to use the Apple Translate APP when discussing academic integrity. Students did not find a ‘final answer’ to the ethical question we explored, but it provided a good opportunity for them to practice speaking and engage in deeper reflection.”