Summary

There is a question sitting underneath most conversations about generative AI in schools right now. Not whether it belongs in the classroom. That debate is largely settled. The harder question is what happens to the parts of learning that cannot be automated.

For this episode of Season 3, Garth Nichols and Jason Rogers put that question to a panel of education experts: Alan Gertner, Justin Medved, Natalie Vera, and Eric Hudson. Rather than a single guest working through a single book, this conversation brings several voices into the room together, each carrying a different vantage point on where AI is already reshaping classrooms and where it still has no business being.

The panel moves quickly, but it stays grounded. Talk of curriculum and tools gives way to something more personal: student agency, the discipline of keeping a human in the loop, and what it might mean for a generation of students to grow up fluent in tools built to think for them.

What lingers longest is a claim offered almost in passing, that real education happens in the parts that cannot be Googled. Not because the tools are unwelcome. Because the parts that cannot be searched are the parts that were never really about information in the first place. Literacy, ethics, leadership. Everything the panel touches keeps circling back to that idea.

Chapters

00:00 setting the stage: why ai matters for schools right now
04:00 the panel introduces itself, and where each perspective differs
09:49 stories of ai already at work in classrooms
20:12 practical strategies for bringing ai into a school
29:59 the ethics of ai, and why a human still needs to be in the loop
40:03 a future vision for ai and school leadership
50:10 helping educators and parents step into ai with confidence
59:57 closing thoughts and where to find more

Key Takeaways

Agency, not automation. The panel keeps returning to student agency as the center of AI literacy. The goal is not a generation that defers to the machine, but one that knows when to.

Inspiration before implementation. The most convincing case for AI in schools comes not from policy, but from small classroom stories. The panel offers several, and they do more work than any framework could.

A human still has to be in the loop. Ethical use of AI is treated as a design constraint, not an afterthought. Every strategy discussed assumes a person is still making the final call.

What cannot be searched. For all the ground the panel covers, the conversation keeps returning to the same idea. The most important parts of education are the parts a search engine was never built to hold.

Quotes

"Centering student agency in AI literacy."
"Real education happens in the parts that can't be Googled."
"Generative AI can push civilization forward."

Resources

Learning on Purpose Newsletter: https://substack.com/learnonpurpose
Cohort21 Blog and Resources: https://cohort21.com
Kinship, Precision Learning: https://buildkinship.ai (double check, see note above)
NETI, Network for EdTech and Innovation: https://linkedin.com/groups/12345678

Guest Links

Justin Medved, LinkedIn: https://www.linkedin.com/in/justinmedved/
Natalie Vera, LinkedIn: https://ca.linkedin.com/in/natalie-vera-3b1ab840
Eric Hudson, LinkedIn: https://www.linkedin.com/in/ejhudson/
Alan Gertner, LinkedIn: [needs confirmation, placeholder]

Summary

Some leadership lessons arrive through frameworks. Others arrive on four legs.

In this episode, Garth and Jason sit down with Robyn Meagher and Jason Dorland, whose book Ike traces the impact one animal had on how they think about vulnerability, joy, and connection. Their journey is not a typical leadership origin story, and that is part of what makes it worth hearing. Ike became a mirror for both of them, a way of seeing self-worth and relationship differently, and that shift carried directly into how they now coach and lead others.

The conversation covers journaling as a practice for processing emotion rather than just recording events, and how psychological safety is not a policy but a condition built through real, sustained connection. Robyn and Jason return often to joy and play, not as a break from high performance but as one of its actual sources. Resilience, in their telling, is less about grinding through and more about protecting the capacity to feel light even under pressure.

What emerges most clearly is the honesty in how they describe vulnerability. Not as a leadership buzzword, but as something they learned had to be lived before it could be taught.

This is a conversation for leaders who sense that the science of performance only gets you so far, and that heart has to be part of the equation too.

Chapters

00:00 introduction to leadership and passion
02:32 the journey of Robyn Meagher and Jason Dorland
05:02 exploring the book Ike and its emotional impact
07:58 the influence of Ike on vulnerability and relationships
10:12 leadership practices: journaling and psychological safety
18:32 the role of fun and joy in leadership
24:59 Ted Lasso: a model for heart centered leadership
28:55 Beards Book Club outro

Key Takeaways

Vulnerability, practiced honestly rather than performed, deepens both connection and leadership effectiveness.

Joy and play are not indulgences. They are part of what sustains high performance and resilience over time.

Animals can reshape how we see our own worth, offering a kind of unguarded reflection that is hard to find elsewhere.

Journaling gives leaders a way to process emotion in real time, rather than letting it accumulate unexamined.

Psychological safety is built through authentic connection, not declared through policy.

Leadership sits at the intersection of science, heart, and lived experience, and none of the three is optional.

Sound Bites

"Animals profoundly impact our self-perception."

"Psychological safety is foundational for growth."

"Joy builds resilience and enhances performance."

Resources

yourmindset.ca - https://www.yourmindset.ca

Summary

What does it mean to achieve something today, when the paths that used to define success are shifting under everyone's feet?

That question sat at the center of our conversation with Asheesh Advani, CEO of Junior Achievement Worldwide and author of Modern Achievement. Ashish has spent his career watching how young people define success, and what he is seeing now is a generation navigating career paths that no longer move in a straight line. AI is part of that shift, not as a threat to outrun but as a tool that, used well, frees people up to do the harder, more human thinking.

Asheesh walked us through a framework he calls fixed, flexible, and freestyle, a way of thinking about leadership and career decisions that accounts for how much certainty we actually have in a given moment. We spent time in the tension between meritocracy and effort, and why rewarding habits and persistence may matter more right now than rewarding outcomes alone. Mentorship came up again and again, not as a program to run but as a relationship that lets people find purpose at their own pace rather than someone else's timeline.

What stayed with us was his point about meta-learning, the idea that reflecting on how we learn is becoming as important as the learning itself. In a world moving this fast, that kind of self-awareness may be the most durable skill a leader or a young person can build.

This is a conversation for leaders and educators trying to make sense of achievement in a world that keeps redefining it.

Chapters

00:00 introduction and updates on Junior Achievement Worldwide05:13 the changing landscape of success and achievement08:19 path dependency and career flexibility10:55 the fixed, flexible, and freestyle framework13:58 meritocracy and the role of effort17:11 generative AI and education19:56 the structure of modern achievement23:18 mentorship and purpose in education26:12 Ted Lasso: leadership lessons29:13 conclusion and final thoughts

Key Takeaways

Success no longer follows a fixed path, which means leaders need frameworks flexible enough to hold uncertainty rather than pretending it away.

The fixed, flexible, and freestyle framework offers a way to match your approach to how much certainty a situation actually allows, rather than defaulting to rigid planning everywhere.

AI is most useful when it takes on the easy tasks and frees people to spend their effort on the hard thinking that actually builds judgment.

Rewarding effort and habits, not just outcomes, builds a healthier relationship to achievement, especially for young people still figuring out what success means to them.

Mentorship works best when it makes room for someone to find their own purpose at their own pace, rather than steering them toward someone else's definition of arrival.

Meta-learning, the practice of reflecting on how you learn, may be one of the most important skills to build in a world that keeps changing what success requires.

Sound Bites

"Use AI to do hard thinking, not just easy tasks."

"We should be rewarding effort and habits, not just outcomes."

"Let people find their purpose at their own pace."

Resources

Modern Achievement by Asheesh Advani - https://www.amazon.com/s?k=Modern+Achievement+Ashish+Advani
Junior Achievement Worldwide - https://jaworldwide.org
Marshall Goldsmith - https://www.linkedin.com/in/marshallgoldsmith
Generative AI tools - https://openai.com/blog/chatgpt
Meta-Learning and Reflection Science - https://www.sciencedirect.com/science/article/pii/S0160289619301244

Guest Links

LinkedIn - https://www.linkedin.com/in/ashishadvani/
Website - https://jaworldwide.org

Keep Leading the Lasso Way.