The following are my top ten things that (when taken together) create a focused snapshot of what I truly believe deserves attention right now.
Right now, there is a lot coming at education leaders all at once. New tools, shifting expectations, crazy policy and funding hits (in the U.S. at least), and real questions about what learning should look like moving forward. It can feel scattered, but there are some clear patterns when it comes to how AI is intersecting across the board. This week I took some time to reflect on what we have learned and where we are more than likely moving with AI and education. The following are my top ten things that (when taken together) create a focused snapshot of what I truly believe deserves attention right now.
Each of the following points to a place where thoughtful, timely decisions can make a real impact for students and educators alike.
1. From Content Mastery to Cognitive Capability
Access to information is no longer the constraint. The differentiator is how effectively students and educators alike can interpret, question, and apply knowledge in AI-supported environments. Instructional models should increasingly prioritize reasoning, synthesis, and transfer over recall.
This is the hyper-thinking I was referring to in a keynote address I gave a few weeks ago.
2. Designing for Human-AI Collaboration
AI is not an add-on tool. It is becoming a persistent partner. The key question is not whether students or educators use AI, but how well they learn to work with it. This requires explicit teaching — not just of prompt articulation, verification strategies, or critical evaluation of outputs but also of curiosity, problem identification, and critical thinking.
3. Assessment Must Evolve or It Will Erode
Traditional assessments are losing validity in environments where AI can generate high-quality responses instantly. And honestly it is about time something pushed us beyond the multiple-choice test. Institutions need to carefully and quickly accelerate movement toward process-based evaluation, performance tasks, and demonstrations of thinking rather than static answers. This is going to push us in terms of how many assessments we give and the flow of how those are assigned.
These things can let students leverage AI while making unique contributions. It is high time we move past thinking that our high point of assessment is a standardized test.
4. The Emerging Importance of Verification Skills
As generative systems scale, the ability to validate information becomes more important than the ability to produce it. Students must learn how to cross-check sources, identify hallucinations, use evidence-based thinking to select and refine sources, and assess credibility across multiple systems.
This isn’t just information literacy; it is scientific literacy coupled with evidence-based decision making.
5. Faculty Roles Are Shifting Toward Orchestration
At every level, educators are moving from primary content deliverers to designers of learning experiences. This includes curating AI-supported activities, guiding inquiry, and ensuring that technology enhances rather than diminishes intellectual rigor.
This includes post-secondary education. But the question at all levels is if educators/faculty will know how to pivot from a focus on content and facilitation to a focus on curation and orchestration. This will take leadership, cross university/school collaboration and national networks to scale.
This is a new way of thinking about the work. Again, mindsets matter.
6. Organizational Agility Is Becoming a Core Capability
Education systems built on fixed schedules, rigid curricula, and slow decision cycles are increasingly misaligned with the pace of technological change. Institutions should examine how quickly they can adapt programs, policies, and practices in response to emerging tools and evidence.
This isn’t about shifting because technology is threatening the status quo, it is about adapting to ways of work. Leaders must start stress testing their ability to pivot and shift — we can’t take too long here; the technology will surpass us. But if actions are grounded in intention and goals it should stick past the change rate of tools.
7. Equity Considerations Are Expanding, Not Narrowing
AI has the potential to both widen and close gaps. Access to tools is only one dimension. Differences in how effectively students are taught to use AI may become a more significant driver of inequity than access itself.
Let me be clear, this is not AI literacy — it is developing the mindsets of curiosity and connected action/decision making that will be stickier than basic how-to skills. Again, with a focus on the humans not the tools that will all change tomorrow.
8. The Redefinition of Academic Integrity
Rather than focusing solely on detection and prohibition, educators at every level must clarify what constitutes appropriate collaboration with AI. Clear norms and transparent expectations will be more sustainable than enforcement alone.
At this point I think it is safe to say that if systems make it easy to sit still there won’t be much worth in the system itself. We have to think about short tail and long tail when it comes to AI and change in our organizations.
9. Preparing Students for AI-Augmented Work
Workplaces are shifting toward decision-making supported by AI systems. Education is included in that shift. There are lessons to learn by incorporating real-world problem solving, scenario analysis, and opportunities for students to engage with AI in authentic contexts. For leaders, that means they need to find the use cases for AI and elevate the stories of what is helping.
I’m not using the “What’s Working” language here intentionally because our focus might need to be on safe and secure not quantifiable impact. We love to measure things, but as any good leader knows sometimes what helps the most isn’t measurable (yet).
10. The Urgency of Institutional Learning
This may be the most important shift. Institutions and educational systems themselves must learn faster. The ability to experiment, evaluate, and iterate on new approaches will determine long-term relevance more than any single technology adoption. Think rapid cycle prototyping here — what would it look like if we leaned into this as a way of living in the work we do in education and reframes our approach to “failing fast” at a more systems level.
As any good kindergarten teacher can tell you — we know what works and what doesn’t near immediately. We need to think about how to systematically test and refine and identify how AI can help us in that work in ways that make it easier to move.
This isn’t about speed it is about process and clarity around knowing and sharing what doesn’t work, not just what does. Side note, the people that emerge as leaders aren’t perfect. Rather they are curious about trying and skilled enough to see the value in sharing failures not just successes.
That’s my list this week — we will see if it sticks!
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