Minding Machines

Abhishek Chakraborty

Minding Machines is a podcast dedicated to fostering thoughtful, open dialogue about artificial intelligence. The show 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. Minding Machines is an independent podcast produced at Lawrence University, Appleton, Wisconsin. The views expressed are those of the participants and do not represent the university. Background music - Relaxing Light Background (short version) by AudioCoffee (https://freesound.org/s/725615/)

Episodes

  1. 6d ago

    Learning by Ear and by Algorithm: A Student Founder on AI, Music, and Startups

    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 (00:40) - Meet Abir Sud (01:47) - Icebreaker (03:22) - Abir's mixed feelings about AI (05:58) - Joining a startup’s founding team (09:09) - What a Music Domain Expert does (11:41) - Who the app is for and how it works today (16:47) - Under the hood: models and training data (20:14) - Feedback and pushback from teachers and investors (22:33) - Where the app is headed (24:45) - AI sentiment at Lawrence (26:21) - How Abir uses AI as a student (28:19) - When AI helps learning and when it undercuts it (30:31) - An open question: personalizing the AI coach (31:53) - Advice for students and faculty (33:41) - What’s next for Abir (34:25) - Closing thanks and season wrap-up

  2. Oct 23

    Thought Experiments Made Real: A Philosopher on AI, Minds, and Learning

    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 detailsSynopsis: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: First 2 Weeks: Investigate aspects of how humans think.Weeks 3-7.5: Explore how generative AI systems work, with an emphasis on hands-on interaction.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 (00:42) - Meet Mark Phelan (01:43) - Icebreaker (03:29) - AI and philosophy of mind (05:47) - Meaning, intentions, and consciousness in LLMs (10:12) - Can machines produce knowledge? (14:21) - An AI philosophy competition (16:01) - Cognitive offloading: where to draw the line (22:27) - Inside Philosophy 111: Computation and Cognition (28:39) - Students who oppose AI, and being transparent about instructor use (32:00) - Advice for Lawrence colleagues and students (35:49) - Low-hanging fruit: getting started with AI (38:39) - Closing thanks and sign-off

  3. Oct 16

    Teaching and Learning Mathematics with AI

    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 AIStephen Raaijmakers, Large Language Models Dwarkesh Patel, The Scaling Era: An Oral History of AI, 2019-2025Tom Griffiths, The Laws of Thought: The Quest for a Mathematical Theory of the MindClick here to view the episode transcript. (00:00) - Intro and welcome (00:43) - Guest introduction: Scott's role and interests (01:40) - Icebreaker (03:28) - Calculators and AI (07:12) - Cognitive offloading: why the purpose of the task matters (10:52) - AI and mathematical research (14:35) - Novelty vs. creativity (18:04) - What’s left for human mathematicians? (21:24) - AI in the math classroom (26:48) - Scott’s own path with AI (31:01) - Syllabus policies and rethinking how coursework is graded (36:05) - AI in intro vs. advanced math courses (40:26) - Can AI help with math anxiety? (42:55) - Advice for colleagues and students (47:37) - What Scott is looking forward to (49:03) - Closing thanks and sign-off

  4. Oct 9

    How the Library Fits Into AI-Era Education

    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 (00:35) - Guest introduction: Colette’s role as Digital Collections Librarian (02:13) - Icebreaker (03:11) - How AI has changed library work (04:43) - Using AI to unstick a research dead-end with students (06:03) - How research appointments work, and how students request them (08:46) - AI use in research appointments (13:00) - Doing this work before AI existed (14:00) - Has AI actually been helpful for this work? (16:08) - Working with faculty: archives, instruction sessions, and LibGuides (20:03) - A tour of general library resources: OneSearch, RefWorks, interlibrary loan (25:05) - Introducing “Navigating AI at Lawrence” and the CARE framework (30:36) - Information literacy vs. AI literacy (34:09) - The ACRL framework and “scholarship as a conversation” (35:52) - Closing thoughts: the library’s role in an AI-saturated environment

  5. Oct 2

    AI Tools in Canvas

    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 (00:44) - Guest introduction: Jedidiah's role in Instructional Technology (01:16) - Icebreaker (01:57) - What an Instructional Designer actually does (03:35) - How AI has changed the work of course design (05:08) - Campus sentiment (07:14) - A tour of Canvas's AI tools - accessibility, quiz authoring, search, discussion summaries, and translation (12:04) - Which tools are actually worth using and where to start (14:45) - Agentic AI and Canvas: what's coming, and what faculty want (18:41) - Does AI undermine academic rigor? (20:32) - Using AI to make room for deeper thinking (21:33) - Has AI actually made Jedidiah's own work more efficient? (26:32) - AI detection tools (29:00) - What to watch for next in Canvas's AI roadmap (31:36) - What Jedidiah is working on currently (33:04) - Closing thanks and sign-off

  6. Sep 25

    Starting the AI Conversation at Lawrence

    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 - mark.phelan@lawrence.eduMike's email - michael.clayville@lawrence.eduSyllabus Policies for GenAI Repository Artificial Intelligence and Academic Integrity: Expectations Worksheet5-level AI Assessment Scale9-level AI Assessment ScaleEthan Mollick’s substack and books - Co-Intelligence: Living and Working with AI and Co-Existence: The Next Phase of AIClick here to view the episode transcript. (00:00) - Intro and welcome (01:04) - Guest introductions: Mike and Mark's roles at Lawrence (01:53) - How the AI Task Force came to be, and where AI stood at Lawrence before it (04:14) - The task force's charge: institutional literacy and faculty perspective (06:29) - The two working areas of the task force — policy and student programming (09:00) - Reading the room: mixed sentiment among faculty, students, and staff (13:54) - Policy, guardrails, and why a one-size-fits-all rule doesn't fit (17:09) - Mike and Mark's personal interest in AI — research angles and everyday curiosity (21:55) - Where co-directors see AI heading next (26:03) - Learning to live with a new technology: lessons from computers and the internet (28:32) - What's next: winter term workshops and future plans (29:12) - What makes a Lawrence-specific response to AI distinct (32:17) - Closing messages: an invitation to conversation

  7. Sep 23

    Welcome to Minding Machines

    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.

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    6 Episodes

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About

Minding Machines is a podcast dedicated to fostering thoughtful, open dialogue about artificial intelligence. The show 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. Minding Machines is an independent podcast produced at Lawrence University, Appleton, Wisconsin. The views expressed are those of the participants and do not represent the university. Background music - Relaxing Light Background (short version) by AudioCoffee (https://freesound.org/s/725615/)

Information

  • Creator
    Abhishek Chakraborty
  • Years Active
    2026
  • Episodes
    7
  • Rating
    Clean
  • Copyright
    ©️2026 Minding Machines