By Lew Ludwig
Recently, my wife and I watched a Columbus Crew victory—from the future. We were in Sydney for the EDUTech_AU conference, and thanks to the wonders of the international dateline, we enjoyed a Thursday-morning triumph before the match even happened in Ohio. The opening Crew goal was a textbook example of "playing to space": one player placed the ball beautifully into open territory, perfectly anticipating his teammate's forward run and leading him directly into a scoring opportunity. This graceful bit of soccer geometry lingered with me through the EDUTech sessions, nudging me to revisit and expand on an analogy I've found fruitful before—but this time, with fresh insights from the world of generative AI.
Real Learning Takes Two
Just like in soccer, playing to space in education takes two. First, the instructor has to read the field—spot the opportunity, anticipate where a student could go next, and deliver a thoughtful pass into that open learning space. But a well-placed ball means nothing if the student isn't moving. They have to see the opportunity too—and make the effort to run onto it. In his opening keynote, Sal Khan put it plainly: no matter how elegant the explanation, learning won't happen unless the student is motivated and engaged. That, he argued, is where much of educational technology stumbles. We pour energy into crafting better and better explanations, expecting understanding to follow. But that's a one-sided game. Real learning takes two—an intentional pass and a committed run.
The Human in the Loop
While it takes two, the responsibility isn't evenly split. As instructors, we don't just play the pass—we coach the team. It's on us to recognize the opening, deliver the ball, and help our students see why it matters. Even if we build the most personalized chatbots imaginable—each one fine-tuned to a student's needs—that alone won't guarantee movement. The teacher still has to coordinate the play, to guide the flow of learning. That means providing context, naming the opportunity, and encouraging students to take the run. This was Sal Khan's message, too: no matter how powerful the AI, we still need the human in the loop—not just to deliver content, but to spark curiosity, build trust, and keep students engaged in the game.
The Screen Time Question
As the Q&A session with Kahn wound down, my wife—who teaches fifth grade and had been listening intently—leaned over with a question she didn't get to ask: "More screen time? Is that really the answer?" Her concern wasn't theoretical. During rainy-day recess, she watches her students instinctively gravitate to their Chromebooks, faces flickering blue in that telltale glow we've all come to recognize. Every time, she finds herself gently coaxing them toward something more tactile—card games, Legos, maker kits, anything that gets their hands moving and their eyes off screens. Given time and encouragement, they'll engage wholeheartedly. But that initial magnetic pull toward the digital is real, and it's getting stronger. Her quiet insight cut straight to the heart of Khan's vision: we can't simply assume that more sophisticated AI will automatically spark deeper curiosity. If we're going to pass the ball into space, we first have to help our students look up from their screens long enough to see it coming.
Making Assignments Worth Looking Up For
This is where the TILT framework—Transparency in Learning and Teaching—becomes essential. If playing to space is about anticipating where students can go, then TILT is how we coach them to see the opening and take the run—even when they're starting with their heads down, absorbed in screens. When students understand not just what they're supposed to do, but why it matters and how they'll be evaluated, suddenly that assignment becomes worth lifting their eyes for.
The framework is simple: for every assignment, we clearly articulate three things—the purpose (why we're doing this), the task (what you need to do), and the criteria (how your work will be evaluated). But don't let its simplicity fool you. Studies have shown that this approach significantly boosts engagement and academic confidence, particularly for first-generation and underrepresented students. And in my AI workshops with faculty, this is often the biggest takeaway.
The Real Transformation: Teaching Why, Not Just What
Here's how this played out in my own classroom. When I used to teach linear algebra four days a week, I had room to weave in applications like Google PageRank, the Leslie Matrix, and the Leontief Model as special projects. It took time to find the right materials—resources that matched the level of my students—but it was always worth it. My course evaluations consistently highlighted how meaningful it was to see theoretical math come to life. These projects helped students realize that linear algebra isn't just abstract machinery—it's the engine behind how we rank websites, model ecosystems, and track economic flows.
But then our university shifted to a three-day a week schedule—fourteen fewer class sessions—and I figured these applications would have to go. I only teach the course every other year, and honestly, it always felt like I was reinventing the wheel. This time, though, I turned to Gemini 2.5 Pro with a simple prompt, and in about ten minutes, it handed me a 22-page report on Google PageRank—well-scaffolded, readable, and aligned with my students' level. It even generated a ten-question quiz and a companion webpage to boot.
But the real transformation came when I used AI to "TILT" the assignment. (Pro tip: ask your AI if it knows the TILT pedagogical framework before you begin—it's a good litmus test.) This is the coaching move. It's what elevates me from being just another player trying to make a decent pass to becoming the one who helps students see the shape of the play. By clearly articulating why this assignment matters, I gave students more than just instructions—I gave them a reason to care. I could now say: "Here's one of the many surprising places an eigenvector shows up. This isn't just a math trick—it's the foundation of how billions of people navigate the web."
Your Move
As I write this from Sydney, halfway around the world from my Ohio classroom, I'm reminded that the future of education isn't waiting for us to catch up. Our students are already living in an AI-integrated world, and they need us to be their coaches—not just their content deliverers. So here's your homework: This week, try one small experiment. Use AI to TILT one assignment. Ask it to help you articulate not just what students should do, but why it matters. See if you can pass the ball into space—anticipating where your students could go next, then helping them see the opening and take the run.
The game is changing, but that doesn't mean we're sitting on the bench. We're still the coaches. We still control the play. And our students are still waiting for us to help them see the beautiful possibilities ahead. The question isn't whether AI will transform education—it's whether we'll step up to guide that transformation.
P.S. This post was written from a hotel room in Sydney, just after an eight-day tour through New Zealand's North Island, including a long-awaited visit to the Hobbiton Movie Set. Fitting, really—my very first There and Back Again column was drafted in a Christchurch hotel on the South Island, fresh from our first Lord of the Rings pilgrimage. That experience is what inspired the name of the column, borrowed from Bilbo's memoir. What began as a playful nod to Tolkien has grown into a genuine journey—through changing classrooms, evolving technologies, and unexpected adventures in AI.
With Kira Hamman stepping down as editor, the MAA is currently seeking someone new to carry Math Values forward. If this turns out to be the final installment for a while, it seems only right to offer deep thanks to Kira—the original steward of this space and the fearless editor behind this column. She saw the potential in these musings, encouraged the metaphor-rich meanderings, and gave me the freedom to explore this evolving intersection of math, teaching, and AI. I am endlessly grateful for her insight, patience, and generous guidance. This column exists because she passed me the ball.
Lew Ludwig is a professor of mathematics and the Director of the Center for Learning and Teaching at Denison University. An active member of the MAA, he recently served on the project team for the MAA Instructional Practices Guide and was the creator and senior editor of the MAA’s former Teaching Tidbits blog.