Rethinking Intelligence in the Age of AI: A Call to Educators

Friends and colleagues in education,

We stand at an inflection point: the AI era is forcing us to confront a deeper crisis—not just about tools, but about intelligence itself. As Van brilliantly argues in her article, “Humanity Is Facing an Intelligence Crisis,” we weren’t taught to engage with intelligence; we were taught to exploit it. 

If we accept that claim, then our curricular design must shift dramatically. Van offers a Leading Intelligence Framework for working with Machine Intelligence—one that I believe can become a compass for how we teach students to think with AI, not just to use it.

The Leading Intelligence Framework, re-cast for classrooms

Here’s a distilled version of Van’s framework, adapted for pedagogical use:

Van notes these levels don’t operate in isolation; the most capable “users” (or thinkers) shift fluidly among them…I find this fascinating.

What this means in practice

Here are three provocations/possibilities for classrooms:

1. Reject the binary mindset Too often students feel: use the AI → you lose thinking, or don’t use it → you stay smart. Van challenges this binary. We must teach the third option: how to lead Intelligence.  We should help students see AI as a mirror, amplifier, and conversational co-thinker.

2. Design assignments by “leading, not prompting” Instead of “Write a 500-word essay on X,” try: “Lead GPT to critique a dominant narrative, then propose alternative perspectives, and reflect on what assumptions you uncovered.” Van suggests that this kind of prompt helps students lead an exploration.

3. Shift assessment from “correctness” to resonance & refinement At Level 3, the feedback structure moves away from just “right or wrong.” Instead, it becomes: What resonates? What iterations worked? What bias or blind spots emerged? Assess how students guide the inquiry, refine questions, and navigate complexity.

 A mini adoption roadmap for a course

1. Audit current assignments — Which ones are purely “fetch & repeat”?

2. Layer in scaffolds — Teach students the art of prompting (Level 1) and prompt refinement (Level 2).

3. Pilot a “think with me” dialogue — Have a week where students must iterate prompts, critique the AI, and co-create a final product (rather than just accept its first pass).

4. Reflect meta-cognitively — Ask: How did your guiding questions change? Where did the AI mirror your blind spots?

5. Share across faculty — These modes are not discipline-specific. Humanities, CTE, STEM, Fine Arts — all can adopt a Leading Intelligence stance.

 A few speculative implications

• Opportunity matters: Students with strong metacognitive and rhetorical skills will leap ahead in “leading intelligence.” We must scaffold that development for all.

• Teacher identity must evolve: We become a guide of inquiry, not just source of answers.

• Institutions will need new rubrics based on “resonance, iteration, and leadership of intelligence” over “accuracy, volume, fluency.”

Final thoughts (for you & me)

The AI moment is not simply a disruption of tools. It’s a challenge to our theories of thinking, knowing, and leading. Van’s Leading Intelligence Framework gives us a vocabulary—and a tentative architecture—for that shift. But it’s our job, as educators, to make it actionable.

Let’s stop pretending we teach “critical thinking” in a vacuum. Let’s redefine the goal of leveraging AI in the classroom.  It’s time to teach how to think with intelligence: human and machine, in partnership. And let’s do it urgently.  AI isn’t just shaping the future of work—it’s shaping the future of thought. Our responsibility is to ensure students don’t just consume intelligence, but learn how to lead it.