Educators keep asking the wrong question—which tool should my students use?MIT Lincoln Lab veteran Dr. Tim Dasey (https://www.linkedin.com/in/timdasey/) argues we should first teach how thinking and learning work (for humans and machines), then use AI intentionally. In this candid conversation, he lays out a practical roadmap for schools: measure what matters, move from waterfall to agile change, and shift student work from low-level tasks to architecture, systems thinking, and judgment.Why watch- A research-to-practice perspective from 30 years at MIT Lincoln Lab (national security, health, disaster response).- Concrete playbook for departments, deans, and teachers who want progress next semester—not in five years.- Nuanced takes on ethics, energy, and bias that move beyond slogans to incentives and action.Highlights- “You don’t have to use AI to teach about AI.”- Replace slow strategic plans with agile experiments.- Stop saying “durable skills” unless you measure them.- Coding is shifting: less syntax, more architecture & abstraction.- Treat the model like a transfer student—teach it your class norms.- Ethics = trade-offs; fix incentives (e.g., metered API usage) instead of preaching.- Prepare sociologists to manage AI agent cultures.- Goal: reduce anxiety, increase agency—students who love learning will be okay.Chapters00:34 — Guest intro: MIT’s Dr. Tim Dasey04:03 — Interdisciplinary mindset; “learning signals” for teams06:36 — Seek variety; prompt the unusual08:27 — Build learning orgs; measure what matters11:10 — “You don’t have to use AI to teach AI”13:07 — Meta-lessons; supervised learning in class15:39 — Make every class improve itself16:39 — Classes of problems; values and ethics18:50 — Energy, bias, and incentives (metered APIs)24:54 — Should kids learn coding? Shift to architecture27:10 — Computational + pattern-based thinking for youth28:41 — Rebalance CS: beyond “95% coding”29:33 — Supervising AI agents; need sociologists33:57 — Tech revolutions favor new grads; empower youth36:00 — Wright Brothers analogy; AI ≠ brain copy38:52 — Treat AI like a transfer student40:05 — Case studies at scale; practice edge cases41:55 — Virtual classes for classroom management42:33 — Wisdom vs. judgment; educate intuition45:12 — K–college pathway: complexity, open-ended problems50:18 — Don’t repeat social media mistake51:27 — Reduce anxiety; nurture love of learningWho it’s forFaculty, instructional designers, department chairs, deans, school leaders, and anyone building AI-ready curricula across the arts, humanities, and STEM.Hashtags#AIinEducation #HigherEd #TeachingWithAI #AIPedagogy #EdLeadership #CurriculumDesign #AgileEducation