Over the years, our column series in TechTrends has explored the intersections of creativity, education, and emerging technologies. Recent articles have examined how GenAI shapes curiosity, how it complicates writing instruction, and what it means to personalize learning at scale. Each piece adds a layer to a larger question we keep returning to: are we even asking the right questions about AI in education?

This latest article grows out of a conversation I was lucky to have close to home. Dr. Danielle McNamara is Executive Director of the Learning Engineering Institute at Arizona State University — and I serve as a director in that same institute. When Sean Leahy and I invited her onto our [Learning Futures podcast](PODCAST LINK), what emerged was a wide-ranging discussion that we knew deserved a longer treatment. That conversation became the basis for this article, co-authored with Danah Henriksen, Lauren Woo, and Sean.

Danielle’s work sits at the intersection of cognitive science, linguistics, and educational technology, and her framing of learning engineering reorients the whole AI-in-education conversation. Rather than asking what AI can do, learning engineering asks: what real-world learning problem are we trying to solve? From there, she challenges the “magic tutor” narrative — not because personalization is a bad idea, but because the real obstacle is time: the fixed semester, the credit hour, the credential. Her vision of AI as an orchestrator of social, cross-disciplinary learning experiences, rather than a one-to-one tutor, is an important reframing for anyone thinking seriously about AI’s role in education.

Henriksen, D., Mishra, P., Woo, L. J., & Leahy, S. M. (2026). Designing for Complexity at Scale: Danielle McNamara on Learning Engineering and AI’s Real Role in Education. TechTrends. Designing for Complexity at Scale: Danielle McNamara on Learning Engineering and AI’s Real Role in Education. https://doi.org/10.1007/s11528-026-01174-5