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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