The Groundhog Day of EdTech | Justin Reich | Director of the MIT Teaching Systems Lab and Educational Researcher at MIT | Season 13 Episode 15 | #236

In this episode, I sit down with Justin Reich, Director of the MIT Teaching Systems Lab, to examine the recurring cycle of technological hype in education. We explore why every new wave of innovation from film strips to smartboards to ChatGPT arrives with promises of transformation, yet rarely produces the sweeping improvements people expect. Justin argues that educational technology follows a predictable pattern: excitement, overreach, weak evidence, and eventual normalization. Large language models may feel unprecedented, but the underlying dynamics are deeply familiar.Our conversation moves into what schools should actually do in moments of uncertainty. Rather than chasing “best practices” for AI literacy, Justin suggests that we don’t yet know what works—and pretending we do may cause more harm than good. He explains why schools must experiment locally, evaluate student work carefully, and resist the pressure to race toward adoption. We also discuss the idea of subtraction in schools: instead of constantly adding initiatives, educators may need to remove practices to make space for thoughtful experimentation.What stayed with me most is the call for humility. Schools are designed to conserve knowledge, not to chase every technological shift. If the past century of edtech teaches us anything, it is that transformation rarely comes from the tool itself. This episode invites listeners to slow down, question hype cycles, and think more carefully about what real learning actually requires.Chapters:00:00 – Introduction01:35 – From Classroom Teacher to MIT Researcher05:00 – The “Groundhog Day” Pattern in EdTech10:05 – Why Technology Rarely Transforms Schools15:50 – AI in Classrooms: Arrival Without Adoption21:30 – The Problem with “Best Practices” for AI27:00 – Experimentation Over Certainty32:15 – Subtraction in Schools: Doing Less to Do Better38:20 – Discipline Differences and Local Context44:00 – Improvement Science and Small Experiments49:30 – Humility, Uncertainty, and the Future of AI52:15 – Closing ReflectionsJustin Reich https://tsl.mit.edu/team/justin-reich/