The Argument Has an Expiration Date

The workforce argument for AI in K-12 is compelling on its surface. A 2025 policy analysis from the Guinn Center for Policy Priorities put the stakes plainly: moving too slowly leaves students unprepared for a changing labor market, while moving too fast exposes them to unintended harms (Guinn Center, 2025). That tension is not new. We have navigated it before. What changes with AI is the pace of the disruption and the scale of the decisions being made at the district level.

The argument that students need AI skills to compete in tomorrow's workforce carries weight. It also carries a history. We said the same thing about word processing in the 1980s. We said it again about internet literacy in the 1990s. Each time, the claim outran our capacity to think carefully about what we were actually teaching and why. The workforce has always changed faster than curricula. That is not an argument against preparation. It is an argument for leaders who can distinguish between readiness and reaction.

Access Was Only Half the Conversation

The equity argument for AI is the one I find most persuasive, and the most easily misread. When we talk about closing the digital divide, we tend to measure success by device counts and connectivity rates. Those things matter. A student without reliable internet access is at a genuine disadvantage. But access to a tool is not the same as meaningful engagement with it. It never has been.

What Chen, Chen, & Lin (2020) documented in their review of AI applications in education holds here: outcomes vary dramatically based not on the presence of the technology, but on how intentionally it is integrated into learning. Personalized tutoring systems produce results when embedded in thoughtful instructional design. They produce noise when deployed as substitutions for instruction the system cannot otherwise afford.

This is the question that computer labs never fully answered. It is the question AI is now putting back on the table.

Leaders Need a Theory, Not a Stance

The most common failure mode I have observed in educational technology adoption is not resistance. It is ambiguous. Leaders who are neither for nor against a new tool, waiting to see what others do, hoping the decision makes itself. That approach has a cost. It cedes the design of the system to vendors, to early adopters, and to whatever political pressure is most immediate.

Digital Promise, in its 2024 AI literacy framework, offers more than a stance: a structured set of leadership actions built around five core questions about AI. What is it? What can it do? How does it work? How should it be used? How do people perceive it? (Lee, Mills, Ruiz, Coenraad, Fusco, Roschelle, & Weisgrau, 2024). Those questions do not resolve the debate. They structure it. That is what a theory of action does. It does not promise certainty. It provides a way of moving forward with intention.

A superintendent who can answer those five questions for their community is not a technology enthusiast. They are doing their job.

This Time Can Be Different

The computer lab story does not have to repeat itself. The principal who asked harder questions before signing the contract. The district that tied AI adoption to a clear instructional purpose before a single student interacted with the tool. The board that demanded a theory of action before approving a budget line. Those stories exist. They do not make headlines because they do not produce dramatic failures. They produce something quieter and more durable: students who are actually being prepared, and leaders who can explain why.

AI is coming into your schools whether you design for it or not. The question is whether the design is yours.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510

Guinn Center for Policy Priorities. (2025). Artificial intelligence in K-12 education: Opportunities, challenges, and policy implications. Guinn Center.

Lee, K., Mills, K., Ruiz, P., Coenraad, M., Fusco, J., Roschelle, J., & Weisgrau, J. (2024). AI literacy: A framework to understand, evaluate, and use emerging technology. Digital Promise.

This week's main article argues that AI leadership requires a theory of action, not a stance. This piece from the Edupreneurs Network picks up that argument from another angle entirely — what happens when the leader doing the integrating is also building something. The edupreneurial mindset, as Luis frames it, is not a personality type. It is a discipline. Adaptability, resilience, a vision that is both boundless and precise, and the willingness to treat every setback as data rather than defeat. Those are not soft traits. They are the operating system behind every educator who has decided that the system as designed is not sufficient and has chosen to build something better. If you are navigating AI adoption in your district while also developing a product, practice, or platform of your own, the overlap between these two conversations is where the real work happens.

Change the way you look at things, and the things you look at change.
— Wayne W. Dyer

Chapter 5, "Digital Transformation and Educational Thought Leadership," addresses the exact tension at the center of this week's issue. Luis argues that effective leaders do not ask whether technology works in the abstract. They ask whether it aligns with their educational goals, their context, and their pedagogical philosophy. That question, applied to AI, is what separates a purchasing decision from a leadership one. The chapter walks through how forward-thinking leaders have navigated the space between technophobia and uncritical adoption, and why the leaders who get it right are the ones who bring a framework into the room before the vendor does.

This week: Read the "Conceptualizing Technology's Role in Learning and Teaching" and "Rethinking AI in Education" sections in Chapter 5. Then ask yourself: Does your current AI adoption have an instructional philosophy behind it, or just a budget line?