Learning doesn't end at graduation, and the systems around us shouldn't either. This clips episode takes that idea and follows it across a whole life. Hosts Ryan Lufkin and Melissa Loble revisit five conversations, from a charity teaching school kids to code in Australia to a summer-learning advocate to a university leader rethinking credentials, and line them up as one continuum: the skills that matter at every stage, and the gaps that open when we treat each stage as separate.

In this episode:

• Fear, not ability, is what keeps kids out of digital skills, which is why Megan Woff's Code for Schools starts with curiosity as it works toward reaching a million students by 2027.
• Summer learning loss hits low-income students hardest, and Christi Kobus Rokicki explains how a gap that opens in childhood compounds every year that learning stops.
• Durable skills like resilience and leadership can't be studied for a test, since Emily Foote argues they're built by doing real work, which matters more as AI absorbs the entry-level tasks graduates used to cut their teeth on.
• The critical thinking and communication skills the liberal arts have always taught, Joe Potvin makes the case, outlast any single job or technology.
• Credentials should recognize the learning people already carry, and Martin Bean lays out how a connected learning system would follow a person across their life instead of ending at a diploma.

Guests:

• Emily Foote, Chief Growth and Strategy Officer, Saxbys 
• Megan Woff, CEO, Code for Schools 
• Christi Kobus Rokicki, Vice President for External Affairs, National Summer Learning Association
• Joe Potvin, History Adjunct Professor, Simmons University, and Senior Portfolio Manager, Cengage 
• Professor Martin Bean
• Katie Novak, Founder and Chief Education Officer (CEO), Novak Educational Consulting

Dale Leszczynski leads RMIT University's Generative AI Lab for Education (GAILE), where he's spent the past year building a skills continuum (instead of a single checklist) for roughly 5,000 educators , on the theory that nobody stays an expert in this field for long. Hosts Ryan Lufkin and Melissa Loble talk with Dale about what happened when other Australian university leaders mapped their own institutions against that continuum, and about the debate RMIT staged at its first education conference: are AI tools morally neutral, or does responsibility for them rest entirely on the people using them? And now that AI can do almost anything, what should humans still do themselves?

In this episode:

• RMIT's AI Skills Continuum treats AI skill-building as a moving target, not a fixed benchmark, since what counts as expert practice keeps changing faster than anyone can master it.
• When Victorian university leaders mapped their own institutions against that continuum, most landed at the earliest stage, a sign of a confidence gap more than a competence gap.
• Efficiency is the wrong goal for AI in education, since it's made his own work more expansive, not faster, which means the work itself has to change instead of just speeding up.
• Students at RMIT's new AI advisory board raised concerns about their own learning unprompted, and the thing they asked for most, across every discipline, was consistency, not more rules.

For further reading:
rmit.edu.au/gaile
https://aiskillscontinuum.com/
https://www.anthropic.com/research/global-workspace

Ryan Lufkin and Melissa Loble step back this episode and hand the conversation to two technologists who interview each other. Mike Mast, Principal Group Program Manager for Microsoft Education and a 1EdTech board member, and Zach Pendleton, Chief Architect at Instructure, trade questions about what interoperability means once AI agents start acting inside the tools educators and students already use.

The starting point: when the client is an agent rather than a person, agents can learn new vocabularies and reason over different systems on their own, so the hard part shifts from rigid data schemas to giving agents a real semantic understanding of a course. Mast points to Canvas by Instructure and its Smart Search beta as one of the few early examples of that idea in practice. Pendleton makes the case that none of this replaces the open standards ed tech already runs on. He frames MCP, LTI, OneRoster, and Caliper as an "and," not an "or."
From there the two get into the part that keeps Pendleton up at night: as actions move further from the human who set the goal, how do we keep people in authority? They cover delegated authorization and consent, audit trails, the gap between what AI can do and what we want it to do, and who carries the cost when an automated tool gets it wrong. 

In this episode: 

• Agentic AI changes what interoperability optimizes for, moving the priority toward semantic understanding of course content rather than identical schemas across systems.
• New protocols build on existing standards instead of replacing them, so prior investments in open APIs and LTI still matter.
• Keeping a human in control sometimes costs a little speed, and both guests argue that maximum speed should not be the goal of an educational AI system.
• Transparency is non-negotiable: if AI touches a grade, students should know, and they should be able to question the result.

James Genone has spent his career at the meeting point of two fields most people keep apart: philosophy and cognitive science on one side, education technology on the other. In this episode, Northeastern University's Senior Vice Chancellor for Learning Strategy joins Ryan Lufkin and Melissa Loble to argue that the design behind a tool decides whether AI deepens learning or just does the thinking for students.

His test is simple: does the AI behave the way a good human tutor would?

Drawing on his founding work at Minerva University, his product role at Atypical AI, and his current advising with the Digital Education Council, Collage AI, and SkillBench, James lays out what learning science actually asks of these tools and where institutions keep going wrong.

In this episode:

• The tutor test James applies to any AI assistant: a good tutor never writes the student's essay, and the AI shouldn't either
• Why engagement is the real engine of learning, and how connecting a subject to what a student cares about drives the attention that learning depends on
• His case against grades, built on an investor analogy: you can't tell two companies apart if all you see is a letter grade, and a transcript does the same thing to students
• The two ways institutions keep getting AI adoption wrong, either leaving faculty to fend for themselves or handing down blanket mandates, and the scaffolded, faculty-led approach he'd put in their place

What makes a skill count, and how does it travel from a classroom or a kitchen table into a job? Ryan Lufkin puts that question to Glenda Quintini, who runs the OECD's Skills and Future Readiness Division in Paris, with Simone Ravaioli sitting in for Melissa. Glenda's answer starts with a confession: a few months ago she couldn't tell critical minerals from rare earths, taught herself by reading and talking to people, and never enrolled in anything. That's how most adults actually learn, and almost none of it shows up on a resume.

The conversation works through what it takes to fix that. Degrees aren't dead, Glenda argues, they're bundles of skills nobody has unpacked. The harder problem is language: employers and educators describe the same skills in completely different words, a gap she says blocks worker mobility as much as visa rules do. From there the three of them get into micro-credentials, individual learning accounts, the data behind dropped degree requirements, and why governments have to move faster than a two-year accreditation cycle.

In this episode:

• Why eliminating degree requirements barely moves non-degree hiring on its own 
• The difference between regulating credentials like New Zealand and letting the market sort it out
• Why the next 15 years will be about lifelong recognition, not just lifelong learning

For further reading: 

• A Skills-First Labour Market

"Hallucinations are not a bug in my classroom. They are the assignment." That's Giorgio Lagna's framing for one of the 13 AI tutors he's built into his biology course at Santa Clara University. In this episode of Educast 3000, the UCSF research scientist and college lecturer joins Ryan Lufkin and Zach Pendleton to walk through what happens when you design AI not to answer questions but to refuse them, not to be right but to argue back. The conversation covers his "Crucible" course design, the DISCO framework research he published this year showing measurable equity outcomes, and why he believes higher ed is making a strategic mistake by treating AI as an integrity problem instead of an assessment problem.

In this episode:

• Toll booth gems, boss battle gems, and certification gems explained
• Why deliberate AI errors teach better AI literacy than warnings about hallucinations
• The cost math on AI-powered oral exams (NYU's run came to 42 cents per student)
• How community college students are now running CRISPR research with AI co-investigators
• What Pope Leo XIV's first encyclical said about AI and education the day before this recording

• Pedagogy Under the Microscope: https://substack.com/@glagna
• Structured AI Integration for Equitable STEM Writing: A Pilot Study of the DISCO Framework: https://www.tandfonline.com/doi/abs/10.1080/0047231X.2026.2625111
• UCSF profile: https://profiles.ucsf.edu/giorgio.lagna

Further readingPope Leo’s ‘Magnifica humanitas’: AI must serve humanity not concentrate power: https://www.vaticannews.va/en/pope/news/2026-05/pope-leo-xiv-encyclical-magnifica-humanitas-ai.html

What does it actually mean to be "evidence-based"? Melissa Loble and Ryan Lufkin sit down with Mary Styers, Director of Evidence and Learning Strategy at Instructure, to find out. With 20 years in program evaluation, Mary breaks down why education's relationship with data is broken and what it takes to fix it.

The conversation covers why rigorous research means nothing if teachers aren't on board, how compliance-driven data cultures stifle real learning, and why a study with only positive results should raise red flags, not applause.
Mary also tackles the paradox of data overload: institutions have more data than ever and still can't act on it. Her answer? Start with one question. Work backward. Build psychological safety. And treat evidence as a journey, not a finish line.

Key topics:

• Why teacher buy-in matters as much as research rigor
• The difference between a compliance culture and a true culture of evidence
• Evidence literacy — what it is and why we're failing to build it
• How rapid cycle evaluation helps institutions iterate fast and learn faster
• Practical first steps for leaders who want to make evidence-driven decisions

In this special clips episode of Educast 3000, hosts Ryan Lufkin and Melissa Loble revisit some of the sharpest conversations on artificial intelligence from recent episodes. Featuring highlights from chats with Sanjay Srivastava (CEO of Vocareum), Joe Potvin (Simmons University and Cengage), Kelly Shiohira (Global Science of Learning Education Network), Matt Winters, (AI Education Specialist at the Utah State Board of Education), and Dr. Joseph Youngblood II (Chancellor of Kean Global), this episode pulls together four distinct perspectives on how AI is reshaping teaching, learning, and the institutions that deliver them. From computer science classrooms to liberal arts seminars, from neuroscience-informed frameworks to transformational learning models, our guests tackle the questions every educator is wrestling with: What should we teach? How do we assess real learning? What's the educator's role now? And what does it mean to prepare students to be responsible citizens in an AI-powered world?

Takeaways:

• AI is forcing education to answer questions it's been avoiding for decades.
• Personalization at scale is one of AI's most promising contributions to learning.
• Authentic assessment matters more than ever when AI can do the work for students.
• The educator's role is evolving, not disappearing.
• AI citizenship is as important as AI literacy.
• Liberal arts skills like critical thinking and communication are more valuable, not less, in an AI era.
• Cultural context, humility, and listening are essential to transformation.
• Frameworks like UNESCO's AI competency guides can help educators navigate the shift.
• Lifelong learning is no longer optional; it's foundational.
• The future of education depends on faculty engagement and institutional flexibility.

Featuring:

• Dr. Joseph Youngblood II, Chancellor of Kean Global
• Kelly Shiohira, director of the Global Science of Learning Education Network
• Joe Potvin, a adjuncy history professor at Simmons University and Senior Portfolio manager at Cengage
• Matt Winters, AI Education Specialist at the Utah State Board of Education
• Sanjay Srivastava, CEO of Vocareum

This episode delves into the transformative potential of experiential and apprenticeship learning models in higher education and their critical role in preparing students for the future workforce, especially amidst rapid advancements in AI. Join us as Emily Foote, Chief Growth and Strategy Officer at Saxby's, shares her insights on how institutions can systemically integrate these models for greater impact.

Main Topics:

• The resurgence of experiential and apprenticeship learning in higher education
• Driving factors behind the renewed focus on experiential models
• The impact of AI on entry-level jobs and skill development
• Transition from knowledge-based to skills-based education
• How to scale experiential learning effectively within institutions
• Developing and validating durable skills like critical thinking, resilience, and leadership
• Measuring success: assessment methods for experiential programs
• Building equitable pathways to participation in experiential learning
• Evolving faculty roles from lecturers to learning architects
• Future of experiential learning in higher education and system-level change

Show Notes:

• Labor market impacts of AI: A new measure and early evidence - https://www.anthropic.com/research/labor-market-impacts
• Shrinking Entry-Level Roles: A Stanford University analysis - https://d2zhjl04.na1.hs-sales-engage.com/Ctc/I8+23284/d2zhjl04/JlF2-6qcW8wLKSR6lZ3pZW5CRPvg52GtDSW26Y9kw5vtDdSW3lfnx11nB5kyW970xhj74Q4H0N5QP790pnqKLW2tMprh6NL3bYW5tKB851lpbDZN699P7cWv1RhW6qTl291_Lx7gW8tjYt54Gw0XPW69HzgF6qMQyZW9gkl1T14Sv-nW64m9pG2G_qrWN6q_63gvbF9HW5VLTdp4-nFFvW58gw-832lhZvW3NgDJw7BsgV9W1QZz5G6qD783W3PMdCJ4rL9WwVwBwj928NdM2W1Whd093G2GwFVrzxYr8fjrG2W1ZYkHC2BFDpyM-kMsm81BkPW8q5y5Z5hWKqBW2wgWW63M45JGW1Tyvt893Gw9nW20M7RR7J_ngKf3LPSfT04
• 81% of graduates believe they are proficient in critical thinking, but only 56% of employers agree - https://www.naceweb.org/career-readiness/competencies/the-gap-in-perceptions-of-new-grads-competency-proficiency-and-resources-to-shrink-it
• CEOs say AI isn't just a tool to help juniors; it is a tool that may eventually replace them - https://www.wsj.com/tech/ai/ai-white-collar-job-loss-b9856259?gaa_at=eafs&gaa_n=AWEtsqeq2qCrXEm-pgwHBybLZX2vTpvCg1mRRu24ZP0dGCFb8GGKvzD5fT8v3vLAOZc%3D&gaa_ts=69c1f94e&gaa_sig=1PfpR7rR1s7nRCxfYDc7HrsIQHRY2CtyOo9VVMdyxLcucxOVLtS-5s1n-tZueQC-iFkWtCsyNocF0Ts1eME9Gw%3D%3D
• Gallup study: Only 11% of business leaders believe higher ed effectively prepares graduates for work - https://www.gallup.com/education/231740/ways-realign-higher-education-today-workforce.aspx?utm_source=chatgpt.com
• Saxbys 2024-2025 Impact Report: https://21774654.fs1.hubspotusercontent-na1.net/hubfs/21774654/Saxbys%20Impact%20Report%20-%202024-2025.pdf 

In this episode, Ryan and Melissa Loble chat with Sanjay Srivastava, CEO of Vocareum, to explore the intersection of technology and education. They discuss Sanjay's journey in the tech industry, the importance of experiential learning, the impact of AI on computer science education, and the evolving role of educators in an AI-driven landscape. The conversation highlights the skills needed for future computer science graduates, the significance of authentic assessment, and the ethical considerations surrounding AI usage in education.

Takeaways:

• Experiential learning environments enhance student engagement.
• Future computer scientists must understand AI's capabilities and limitations.
• Personalization in education is a key benefit of AI.
• Authentic assessment is crucial for measuring student mastery.
• Teachers play a vital role in integrating AI into the classroom.
• AI can help reduce failure rates in education significantly.
• Ethical considerations in AI usage are paramount.

Show Notes:

• How are developers using AI? Inside our 2025 DORA report: https://blog.google/innovation-and-ai/technology/developers-tools/dora-report-2025/#:~:text=Massive%20adoption%20meets%20major%20productivity,of%20AI%20on%20code%20quality
• Vocareum @ Better Together: California Convening on AI in Higher Education: https://www.youtube.com/watch?si=HMfACp4h06PaFvFt&v=hZBYfvG7N7k&feature=youtu.be