Megan DaCosta teaches fourth grade math and science in Windsor, Connecticut. This is her first year bringing AI into the classroom in a real way, and she joined Bob and Jess to talk about what that looks like with 9 and 10-year-olds.

Her district runs on paper and pencil at the intermediate level, on purpose. So Megan's challenge isn't more screen time. It's making the technology she does use count. She works with Magic School AI, building chatbot rooms and station activities her students move through while she stays close.

The best moment of her year came from a mistake. A student practicing multi-digit multiplication got the wrong answer from a chatbot, and it argued with her. They solved the problem four different ways. When the student explained their reasoning, the chatbot backed down and admitted the student was right. The student still brings it up. They beat the robot, and the sudent knows it.

That's the AI literacy lesson hiding inside a math problem. Don't accept the first answer. Question it. Prove it.

In this episode:

• Using AI as a thought partner instead of a replacement for teaching
• Reaching 40-plus students a day without losing your read on each one
• Designing lessons backward so every skill level gets what it needs
• Teaching healthy skepticism without breeding cynicism
• Why she wants students to keep their creativity, not flatten it

Dr. Nicole Danishevsky spent 27 years teaching middle school math and leading district-level curriculum work before stepping into higher education this year. Now an assistant professor at Southern Connecticut State University, she's preparing the next generation of teachers to walk into classrooms where AI is already part of the landscape.

In this conversation with Bob Hutchins, Phd and Dr. Jessica White, Nicole talks about what she's seeing on both sides of the desk. As a parent of a high schooler and a college student, she's watched the early "sign this paper saying you didn't use AI" moment evolve into something more thoughtful. As a former district leader, she's lived through the messy work of getting policy, training, and culture to move at roughly the same speed.

The discussion gets into something we keep coming back to on this show: ban culture creates mistrust and pushes students underground. It also widens equity gaps. The six hours a student spends in school may be the only time they have access to thoughtful guidance on how to use these tools well. Take that away, and you've handed the advantage to kids whose parents already pay for tutors.

Nicole brings a math educator's lens to the question of AI as confidence builder, especially for girls in STEM. She makes the case for using AI to break concepts down, offer real-world entry points, and act as a kind of always-available study partner. Not a replacement for teachers. An extension.

We also talk about onboarding new teachers, vertical scaffolding from kindergarten through high school, and what a truly AI-ready school system looks like from a leadership perspective.

In this episode, Bob and Jess sit down with Zahra Amed to explore the fluid boundary between K-12 education and the rigorous expectations of higher education in the age of generative AI. Zahra brings a unique perspective, having moved from school programs at children's museums to training faculty at Harvard.

The conversation moves beyond the technical mechanics of AI. It focuses on the human elements that technology cannot replicate: social-emotional learning, restorative practices, and the "durable" skills of judgment and critique. She explains why we must treat AI as a "makerspace" for tinkering rather than a repository of answers, and how institutional "walled gardens" can help close the emerging digital divide.

Metacognition and the Baseline Shift

The entry point for college students is shifting. It is no longer enough to arrive with information; students must arrive with an awareness of their own thinking.

The Metacognitive Question: Students should ask, "What is AI doing for me, and what am I still responsible for?"

AI as a Thinking Coach: Moving from "Recall" to "Refine," using AI to fill gaps and expand on original thoughts rather than replacing them.

Durable vs. Vulnerable Tasks

How do we protect learning that requires human reasoning?

Vulnerable Tasks: Processes or formulas that AI can automate without deeper understanding.

Durable Tasks: Human judgment, transfer of knowledge, and original critique.

The Shift in Assessment: Harvard faculty are beginning to grade how students explain and critique AI-generated ideas, rather than the raw output itself.

The Digital Divide 2.0

Equity is no longer just about having a laptop; it's about the quality of the intelligence you can access.

Premium vs. Free: The widening gap between students using advanced paid models and those on inferior versions.

The Walled Garden: Harvard's "AI Sandbox," a secure internal platform that provides equitable access to faculty and students while maintaining data privacy.

Upskilling through Modeling and "Play"

Resistance to new technology often stems from a lack of practical exposure.

The 7-Day Rule: Professional development only sticks if it is applied to a real task (like syllabus design) within a week.

Live Tinkering: The most effective faculty workshops involve live modeling—demonstrating the "messy" process of prompting and refining in real-time.

Dr. William Rice steps into the Executive Director role at ACES this summer. His philosophy is clear: leadership is an embodied practice requiring physical presence and proximity. In this episode, we discuss protecting the human element of education as technology grows increasingly sophisticated.

The Meaning of Models Drawing from his background as a chemical engineer, Dr. Rice views math as building models to understand the world, rather than procedural calculation. Machines handle the heavy computation now. If we simply reward students for playing in a procedural sandbox, we leave them unequipped for a reality where human context separates meaningful work from automated noise.

Observation in Special Education Technology offers a unique kind of support in special education by tracking massive volumes of daily observational data. It helps identify long-term trends a busy educator might miss. Still, a machine cannot replace the physical intuition and empathy of a teacher interpreting subtle, non-verbal cues.

Navigating the Software Flood School districts face a constant barrage of new applications. Dr. Rice suggests a deliberate pause to avoid tool creep. Evaluating new technology must prioritize compliance and rigorous alignment with the agency's mission. Foundational AI literacy matters more than a fragmented landscape of apps; we must understand how these systems function and where their biases lie.

A Question for Reflection: When evaluating the digital tools in your own work, how are you ensuring the technology serves the human context rather than replacing the productive struggle of learning?

Guests Dr. Jess White and Tim House join Bob Hutchins for a conversation about what's actually happening with AI in schools right now.

In This Episode

The three of us recently spoke to a group of student teachers at Sacred Heart University in Connecticut. Around 50-60 seniors, all preparing to enter classrooms. When asked if they'd received any formal AI training or integration frameworks, not a single hand went up.

That moment set the tone for everything we talked about in this episode.

We get into why the gap exists, what it looks like in practice, and what educators can do about it now.

What We Covered

The AI training gap Future educators are entering classrooms without formal AI preparation. The training that does exist tends to stay at the theory level. It doesn't go deep enough to be useful in a real classroom.

The "cheating" question One student teacher asked how to handle job interviews when a district might view AI use negatively. That question told us a lot. Many educators want to use these tools but feel caught between what's practical and what's politically safe.

AI across grade levels Jess breaks down what AI literacy actually looks like from kindergarten through high school.

Tool breakdown: LLMs Each of us shared where we land on the ChatGPT vs. Claude vs. Gemini conversation, and more importantly, why different tools serve different purposes.

Vibe coding and what's coming The ability for everyday teachers to build their own tools is closer than most people think. That changes the economics of educational technology significantly.

The CRAFT Prompting Framework Jess walks through the ACES Center for AI prompting model:

C = Context

R = Role

A = Audience

F = Format

T = Task

Resources

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In this episode of Future Proof Education host Bob Hutchins sits down with Myriam Da Silva, the CEO of CheckIT Learning and the architect behind the AI neuro mentor known as Cleo. Myriam shares her journey from being a struggling student who found her own path to learning, to becoming an educational leader focused on the intersection of neuroscience and technology. The conversation explores how we can move beyond traditional curriculum completion to focus on the underlying mechanics of how the brain actually processes information.

https://www.checkitlearning.com/

The Market's New Demand: Why industry leaders are prioritizing conflict resolution, priority sorting, and emotional intelligence over software proficiency.

The "Clunky" Nature of Change: How institutional evolution is rarely gradual, usually requiring a crisis or a significant shift in data to force a new paradigm.

AI in the Trades: A look at how entrepreneurs in blue-collar industries are using AI to build training databases and improve field operations.

Killing the Written Exam: Drew's innovative approach to "oral defenses" in crisis communication, where students must defend their strategies in real-time rather than submitting a paper.

The Difference Between Output and Outcome: Shifting the metric of success from "how fast can we do a task" to "how much have we actually grown as leaders."

Season 2 opens with a raw and timely conversation between Bob Hutchins and Tim Howes about one of education's biggest challenges: academic integrity in the age of generative AI.

As schools race to adapt, many are responding with surveillance and bans, but Bob and Tim ask a deeper question-what if the problem isn't AI, but the system itself?

They explore how generative tools are not creating dishonesty but revealing cracks in outdated assessment models, why detection software erodes trust, and how educators can rethink learning through transparency, reflection, and prompt literacy instead of punishment.

Redefining Academic Integrity:
Integrity is no longer about "doing your own work," but about demonstrating your own judgment and transparency in using AI responsibly.

Policing vs. Trust:
Detection tools and AI surveillance create a culture of suspicion. True integrity grows from relationships, mentorship, and open dialogue.

Systemic Rot in Education:
AI didn't cause dishonesty—it exposed how transactional learning and grade-focused systems fail to nurture genuine understanding.

Prompts as the New 'Show Your Work':
Instead of grading AI outputs, teachers can assess the quality of a student's prompts, which reveal depth of knowledge and critical thinking.

Inequity and the AI Divide:
Well-resourced schools teach AI fluency. Others teach avoidance. Without intervention, AI will widen the digital divide between students who learn to use it creatively and those who are punished for it.

"Detection doesn't build honesty. Relationships do." – Tim Howes

"If AI can do the task, maybe it's the wrong task." – Tim Howes

"Integrity wasn't working before AI. Generative tools just made the cracks visible." – Bob Hutchins

"Grading the prompt, not the output, reveals the student's understanding." – Bob Hutchins