Minding Machines

In the Fall 2026 season finale, Minding Machines welcomes its first student guest. Abir Sud is a Lawrence senior double majoring in business and entrepreneurship and music, and the Music Domain Expert on the founding team of PlaiMusic Inc. We talk about ZumaPiano, an app that turns piano audio into sheet music and waterfall notation, and the AI practice coach the team is building to support music teachers rather than replace them. Abir also gives a candid student view of AI: mixed feelings about jobs and creative work, how attitudes differ between the Lawrence college and the conservatory, and where AI helps learning versus where it cuts it short.
 

Episode Resources

• PlaiMusic AI and ZumaPiano [link]
• ZumaPiano for iOS [link]
• ZumaPiano for Android [link]

Click here to view the episode transcript.

• (00:00) - Intro and welcome

Mark Phelan, Professor of Philosophy, Director of Lawrence’s Cognitive Science program, and Co-director of the AI Task Force at Lawrence, joins the show to discuss why large language models may be the biggest development in philosophy of mind in a generation. We also discuss whether LLMs can mean, intend, or know anything, the debate over AI-written philosophy, and the real risk of cognitive offloading, including what to hand to AI and what to keep for ourselves. Mark also takes us inside Philosophy 111: Computation and Cognition (offered in the Winter 2027 term) and shares advice for faculty and students on engaging with AI thoughtfully rather than reflexively.

Episode Resources

• Mark Phelan's website [link]
• Emily Bender [link]
• Paper that includes the octopus example Mark referenced [link]
• Journal article in Philosophy and Public Affairs written by AI [link]
• AI Philosophy Competition [link]
• Mark's presentation Can an LLM Have Communicative Intentions? at AI and Philosophy Conference, University of New Orleans, April 16, 2026 [link] and 6th International Conference on Philosophy of Mind: Artificial Intelligence, University of Porto, Portugal, May 6, 2026 [link]
• Why Smart People Make Weak Arguments About AI by Josh May and The Mind and Morality Lab [link]
• AI as Normal Technology by Arvind Narayanan & Sayash Kapoor [link]
• What We Talk to When We Talk to Language Models by David J. Chalmers [link]
• PHIL/COSC 111 course details

Synopsis:
This course is a blend of philosophy, cognitive science, and hands-on AI experimentation. The main goal of the course is to compare human thought with whatever computational generative AI systems do, exploring both their similarities and differences. To do this competently, we’ll first need to explore some aspects of human cognition. We’ll also need to spend time directly engaging with generative AI systems and critically analyzing how they respond to and are shaped by prompting.

The course is structured around three main activities:

1. First 2 Weeks: Investigate aspects of how humans think.
2. Weeks 3-7.5: Explore how generative AI systems work, with an emphasis on hands-on interaction.
3. Weeks 7.5-10: Philosophical reflection on similarities and differences between human and AI cognition. 

Learning Outcomes: At the end of this course, you should be better able to…

• Understand fundamental features of human thought and minds. 
• Interact with and appreciate the strengths and limitations of generative AI systems and large language models.
• More deeply appreciate the range of possible minds and the differences and similarities between human minds and generative AI systems. 
• Be better able to recognize what evidence is relevant for theoretical positions and arguments (and what is irrelevant). 
• Engage philosophically with cognitive science and computer science.

Click here to view the episode transcript.

• (00:00) - Intro and welcome

Scott Corry, Patricia Hamar Boldt Professor of Liberal Studies and Professor of Mathematics, and Chair of the Department of Mathematics, Statistics, and Computer Science at Lawrence, joins the show to discuss whether AI is just the next calculator. We also discuss AI-generated counterexamples to long-standing conjectures, what creativity means in mathematics, and how Scott is rethinking his courses for the new term. He also makes the case for using AI to explore what you’re curious about, which sent him back to the library more than before.

Episode Resources

Scott's reading list on AI includes

• Ethan Mollick, Co-intelligence: Living and Working with AI
• Stephen Raaijmakers, Large Language Models 
• Dwarkesh Patel, The Scaling Era: An Oral History of AI, 2019-2025
• Tom Griffiths, The Laws of Thought: The Quest for a Mathematical Theory of the Mind

Click here to view the episode transcript.

• (00:00) - Intro and welcome

Colette Lunday Brautigam, Digital Collections Librarian at Lawrence, joins the show to talk about what AI actually looks like in day-to-day library work — from helping students track down hard-to-find sources to the library’s own CARE framework for research with AI. The conversation covers everything from interlibrary loan to AI literacy, and makes the case for why the library’s role might matter more, not less, in an AI-saturated educational environment.

Episode Resources

• Navigating AI at Lawrence lib guide [link]
• CARE Framework Tab on the Guide [link]
• Lib Guides by Course [link]
• Library Home Page [link]
• Interlibrary Loan [link]
• Make a Research Appointment [link]

Click here to view the episode transcript.

• (00:00) - Intro and welcome

Jedidiah Rex, Director of Instructional Technology at Lawrence (congratulations on Jedidiah's promotion since the recording), joins the show to talk through what AI actually looks like inside Canvas right now — from accessibility tools to discussion summaries to the agentic course tutors on the horizon. The conversation moves from the practical (which tools are worth turning on) to the bigger question underneath: how do we use AI to create more room for human connection, not less.

Episode Resources

• AI tools in Canvas [link]
• Wise AI book [link]

Click here to view the episode transcript.

• (00:00) - Intro and welcome

The guests for the first episode of Minding Machines are Mark Phelan and Mike Clayville, Co-Directors of the AI at LU (Lawrence University) Task Force. Mark is a Professor of Philosophy and Director of the Cognitive Science program; Mike is a Professor of Practice in the Business and Entrepreneurship program, a performing musician, and a trombonist. We discuss how the task force came to be, what its charge actually looks like, and how they're thinking about AI's effects on teaching, writing, and campus life more broadly.

Episode Resources

• Mark's email - [email protected]
• Mike's email - [email protected]
• Syllabus Policies for GenAI Repository 
• Artificial Intelligence and Academic Integrity: Expectations Worksheet
• 5-level AI Assessment Scale
• 9-level AI Assessment Scale
• Ethan Mollick’s substack and books - Co-Intelligence: Living and Working with AI and Co-Existence: The Next Phase of AI

Click here to view the episode transcript.

• (00:00) - Intro and welcome

Minding Machines is an initiative dedicated to fostering thoughtful, open dialogue about artificial intelligence. The podcast brings together educators, students, and practitioners to explore how these tools are changing the way we teach, work, and think. Our conversations focus on practical experiences, emerging questions, and diverse perspectives across disciplines rather than establishing rigid "right or wrong" answers or settling debates about AI. Thank you for joining us. 

Click here to view the episode transcript.