What EdTech Gets Wrong About Implementing AI

Building AI literacy into a school system or a curriculum is a lot to carry. The tools keep changing, and every plan you write has to account for technology that will look different by next year. So it’s natural to wonder which parts of that plan will last, from the concepts students learn to the examples in each lesson and the policies behind them.Chuck Melley, Senior VP of Policy, Advocacy and Communities at Code AI, explains which parts of an AI literacy plan belong at the center and which need regular updates so that you can plan with more confidence.

How can core curriculum support personalized learning without creating more work for teachers? In this episode of Tech in EdTech, Olivia Lara-Gresty is joined by Kelly Philbin, PhD, Director of Core Adoptions at IXL Learning, to discuss how to connect instruction, assessment, differentiation, and practice. They explore how assessment can help teachers understand student needs and decide what to do next, while keeping students connected to grade-level learning. The conversation also examines support for multilingual learners, the balance between curriculum coherence and teacher flexibility, and what product teams can learn from educator feedback. 

Textbooks have always been static, with the same page providing the explanation. It never takes into account who's reading. Dakshay Mehta, Head of Technical Staff, Stealth AI, joins Eric Stano to talk about what happens when a book becomes a conversation instead. They dig into how AI tutors can adapt to different learning styles and how sequential curricula need to take a step back in modern learning.

What should a healthcare simulation program build before investing in more technology? Francisco Castelblanco, DNP, RN, Chair, Continuing Professional Development & Area Health Education Center Director at MAHEC, explains why educator capability should shape modality choices, scenario design, simulation debriefing, and the measurement of patient outcomes.

Somewhere between the hype and the skepticism around XR lies a clear-eyed view of where it fits within workforce learning.Bharani Rajakumar, Founder, Transfr, joins Zahra to talk through where XR earns its place, where it doesn't, and what every workforce and education leader should consider before their next investment. 

Before your next AI strategy conversation, ask yourself this: Are you starting with implementation, or readiness? Stephen Jull, Global Head of AI and EdTech at Teach For All, argues that schools may be starting in the wrong place. The real question is not just how to roll out AI, but whether the institution is ready for it.In this Tech in EdTech episode, Stephen joins Sean Strathy to discuss why AI readiness must come before rollout, and what schools need to get right before making their next major AI decision.

Enrollment trends may look like they’re improving, but the bigger challenges in higher ed haven’t changed. David Brunner speaks with Phil Hill about the realities behind enrollment shifts, evolving student expectations, and how institutions should rethink technology, strategy, and partnerships. 

Some students log in, submit assignments, and still drop out. So what are we really measuring? In this episode, Shaunak Roy explains why most engagement metrics fail to predict retention and what institutions should be looking at instead. From early risk signals to faculty workload and AI hype, this conversation challenges how higher ed thinks about engagement and what it takes to keep students on track.

Most career readiness programs start in senior year. By then, it’s already late. Hiring is shifting toward demonstrated skills, work samples, and real evidence of capability, while schools still rely heavily on GPA and transcripts. So where is the disconnect? In this episode, Zahra sits down with Allison Danielsen, CEO of Tallo, to talk about skills-based hiring, early career exposure, and why making student talent visible to employers is becoming one of the biggest challenges in education.

Thomas Cavanagh, Vice Provost for Digital Learning at the University of Central Florida, breaks down how analytics should drive retention, graduation rates, and student success. He explains why adoption metrics miss the point and how LMS data can act as an early warning system for timely student support.This episode offers practical insight on using data and AI responsibly while keeping decisions student-first.