“There are a lot of folks out there who are comfortable pretending that just because someone’s butt is in a chair in a lecture hall, that person must be learning.”

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As a cognitive psychologist and Senior Vice President of Research & Analytics at Amplifire eLearning, Matthew Hays, Ph.D., has spent much of his career exploring the gap between how people think they learn and how they actually learn.

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In EP56 of Beyond the Blueprint, he joins Tim Jones and Kris Baird to talk about how people learn, what helps knowledge stick, and how healthcare organizations can get a better return from the billions of dollars invested in training each year. 

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Performance during training can be misleading. Matt uses a free-throw experiment to explain why. Practicing the same shot repeatedly produces better results during practice. Varying the distance forces the learner to make new adjustments with each shot. When tested later, the varied practice produces better performance, including from distances that weren’t practiced. Matt describes the difference as practicing the correction to the last mistake versus practicing the underlying skill.

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Similar patterns show up in learning. Spacing material over time, retrieving information from memory, and delaying feedback can strengthen retention even when the experience feels harder. What feels productive during training doesn’t necessarily predict what will stick.

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Clinicians also arrive at training with different specialties, levels of experience, and existing knowledge. Matt describes conventional instruction built for everyone at once as “one size fits none.” Some learners already know the material. Others are uncertain. Some may hold incorrect information with complete confidence.

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Confidence becomes another piece of the learning process. Someone who knows they are uncertain can seek more information. Someone who is confidently wrong has little reason to question what they know. Matt discusses measuring confidence alongside knowledge to uncover misconceptions that might otherwise go undetected.

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The conversation extends into how health systems measure the return on training. Course evaluations and post-training assessments capture what happens close to the learning experience. Matt connects the investment to what happens afterward in clinical practice. Training designed to reduce infections, falls, or other patient safety events can ultimately be evaluated against those outcomes.

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AI introduces another set of questions. Matt’s research has explored how computers can help people learn faster, remember longer, and transfer knowledge more broadly. Generative AI can support more personalized learning, but it can also do cognitive work the learner might otherwise have to do. As Matt notes, “the way the brain learns hasn’t changed in the last five years or 50 years or 500 years.” 

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For healthcare leaders evaluating their own training programs, Matt brings the discussion back to the design of the learning itself:

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“Anyone who’s willing to believe that this sort of one size fits none training is going to make a difference for learners with a wide variety of experiences and different areas of subspecialty, even within EHR training or within their clinical practices, on some level has to know that they’re doing a disservice to all of the learners in the room at the same time.”