BU Wheelock: What’s the biggest issue that educators using AI are wrestling with right now?

TJ McKenna: There are legitimate concerns about AI that go well beyond education, but what I hear from the educators I work with every day is more specific: Where does AI actually help students learn, and where does it just make things faster? Those are not the same thing. Making things easier is not the same as helping students learn better. 

Educators can see that AI is a genuinely powerful resource, but they are also being flooded with products that promise to solve everything. The real challenge is figuring out where AI deepens learning and where it shortcuts it. Most educators have not been given the time or support to work through that carefully. They have the expertise to make those calls—what we need to do is create the space for them to do it.

BU Wheelock: How can educators help shape the future of AI? 

TJ McKenna: We have to lead with one question: What is best for the student? Then we work backward from there: What environment do students need to learn, and how might AI support that? Right now, a lot of AI tools in education are being designed without deep understanding of how people actually learn. When we forget decades of research on cognition, motivation, and development just because a product looks impressive, we lose the plot. Educators are the ones who understand learning. We should be driving these decisions, not reacting to them.

BU Wheelock: Why is it important that students and educators recognize AI is a tool and not a means to an end? 

TJ McKenna: I actually want to push back on the framing a little, because the language we use to describe AI matters. We explore this in our Historical Perspectives and Design Approaches for Learning course. Calling AI “just a tool” can make it sound neutral, like a calculator or a textbook. But AI is not neutral. When a student, even a very young student, sees AI produce a polished answer, it is easy to assume something intelligent happened. But AI is pattern-matching against its training data—it does not think. 

So if you ask it to “make this lesson better,” it is going to pull your lesson toward the average of everything it has seen. That is not improvement. That is regression to the mean. Experienced educators know what a strong lesson looks like. AI can be powerful for pushing your thinking, but only if you deliberately use it that way and only if you understand what it actually is. That understanding is a big part of what this program is designed to build.

BU Wheelock: What does the new AI and education program offer educators? 

TJ McKenna: This program is built specifically for the professionals doing the work: teachers, instructional coaches, principals, district leaders. We are not teaching you everything there is to know about AI—the technology is going to keep changing. What we are focused on is the question that does not change: How do we improve learning? 

Every course is built around a real tension that educators are navigating right now. Where does AI support real thinking and where does it replace it? How do we redesign assessment when AI can produce something that looks polished but tells us nothing about what a student actually understands? How do we read data in ways that show us actual student thinking rather than just engagement metrics? How do we build policy that protects students without shutting down innovation? You bring the professional knowledge and your context. We help you build the capacity to lead through all of it, grounded in how people actually learn.

BU Wheelock: What will educators learn about how to support the use of AI in the classroom? 

TJ McKenna: One of the biggest things educators need, and rarely get, is sustained time to think critically about these tools. A one-hour PD session is not enough. This program creates a longer-term space where we can dig into real case studies together: how a school integrated AI, what worked, what did not, and why. Across the program, educators learn to design instruction and assessment that holds up in an AI world, read data for real learning rather than surface engagement, build policy that is grounded in evidence rather than fear, and lead implementation that centers equity and student agency at every level. 

We also help educators develop the skill of teaching students to evaluate AI the way we would teach them to evaluate any source, asking whether it is reliable, what is missing, and what biases might be baked in. Those are exactly the kinds of discussions that should be happening in classrooms right now. And they can happen from anywhere. This is a fully online program, designed so that a teacher in Atlanta, a principal in São Paulo, and an instructional coach in Boston can sit in the same room together and figure this out—because nobody should have to navigate this alone.