It Could but Probably Can't: Avoiding Pitfalls of Current AI Model Usage in K12 Education

AI will be a part of children's futures in ways we cannot fully understand. This easily accessible and powerful new technology is imperfect and yet could improve student outcomes for all children in ways that much of education technology has failed to do for the past two decades. But can it?

• What can it do?
• What can it do well?
• What can it do well enough?
• In which contexts and with which students can it do well enough?

Through a equity-oriented K12 public school lens, come and learn why there currently is scant evidence for actual improvements to student learning, how to identify the kinds of education tasks the GPT-style AI models are more likely to struggle with, and grapple with potential unintended consequences of their use.

Join the conversation with a review of current AI research and discussing tangible cases and real-world applications in public education. As with all technology, it will likely improve, but until we know it is good enough, understanding some of the current shortcomings of AI in K12 education applications is necessary for navigating questions now and as AI products continuously evolve.

Michael Hardy is an award-winning public school teacher, principal, and superintendent. He currently researches AI algorithms and measuring student outcomes at Stanford University and is the Executive Director of Learnest, a non-profit organization dedicated to more ethical uses of AI in education.

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