In this special bonus episode, we step away from our typical one-on-one interview format to share excerpts from a student panel that our host Amanda Irvin moderated at the recent "Reimagining Teaching and Learning in the Age of AI" Forum. This event was a joint effort coordinated by the Columbia Center for Teaching and Learning, the Data Science Institute, Columbia Alliance, the School of Engineering, and Teachers College.

Four Columbia students, representing a range of disciplines, backgrounds, and relationships with AI, reflect candidly on what it's actually like to be a learner in this moment. They share how AI functions in their academic lives, what they wish faculty understood about their use of it, and what they need from instructors as the landscape continues to shift. The conversation is less a debate about AI and more an exploration about the relational fabric of learning.

A full transcript with speaker attributions and bios, as well as a link to the video recording, are available in the show notes.

In this episode, we sit down with Dr. Madisson Whitman, Director of Undergraduate Studies and Assistant Director of Curriculum Development at Columbia University's Center for Science and Society, and Lecturer in the Department of Anthropology. Drawing on her work in Science and Technology Studies (STS), Whitman challenges one of the most pervasive assumptions of our moment: that AI in higher education is a foregone conclusion.

In her recent letter to the editor in the Columbia Spectator, a student-run campus newspaper, Whitman offered a direct rebuttal to the sentiment that "AI is here to stay, so what does that mean for Columbia?" Instead, she invites us to resist the sense of "technological inevitability" that pervades so much of today's academic dialogue and to ask what we might be foreclosing when we don’t question AI’s presence in education.

Together, we trace the through-lines between pandemic-era surveillance, "dysfunction creep," and the quiet ways AI is being folded into the learning management systems. We also consider what it looks like to teach with AI rather than through it. Dr. Whitman reminds us that progress is never as linear as it's sold. Educators must keep learning at the center of the conversation, even when urgency and marketing do their best to crowd it out.

Other materials referenced in this episode:

"AI Is Here to Stay: What Does That Mean for Columbia?" — Columbia Spectator

"Letter to the Editor: AI Is Not Inevitable" — Madisson Whitman

A Rant About Technology — Ursula K. Le Guin

In this episode, we talk with Dr. Lucy Appert, Senior Director of Teaching Excellence & Innovation at NYU Arts & Science, and host of the new NYU Office of Teaching Excellence and Innovation’s podcast, What Learning Looks Like. As an academic with 25+ years of teaching experience and a deep commitment to student-centered practices, Lucy shared with us her insights on what learning truly means in an age of AI-driven "efficiency."

Together, we discuss a key problem in higher education: while educators may accept the messy, developmental nature of learning, students are being marketed an idealized reality where AI-supplemented education is frictionless and instantaneous. The What Learning Looks Like podcast offers a counter-messaging to this misleading EdTech and AI marketing. Instead, true learning involves struggle, synthesis, and personal transformation. Lucy also challenges one of higher education's most persistent “Dead Ideas”: that we cannot change. From pandemic pivots to new faculty communities exploring AI in the classroom, it is clear that higher education is very capable of fluctuation and change.

Explore the What Learning Looks Like podcast: https://podcasts.apple.com/us/podcast/what-learning-looks-like/id1839490516

Other materials referenced in this episode:

Learning Objectives & Bloom’s Taxonomy