When a room full of educators pulled up an AI-generated content and someone said, "This is actually really good," the collective nod in agreement was the problem. The output was polished, organized, and used exactly the right vocabulary, and that is precisely what made it worth questioning.
Research on "metacognitive laziness" suggests that when AI makes the work look finished, we stop asking whether the thinking is actually done.
For CS educators designing lessons where AI is present, this distinction matters more than ever. As we continue our second discussion TRACE framework, learn how we incorporate Rigor as an essential construct of embedding AI in lesson.
Read What a Room Full of Educators Taught Me About 'Rigor' in AI and subscribe if you want to receive some of our free resources for piloting the TRACE model.
Tell us more about your experience with rigor with AI in education? Have you encountered the artificial finish line when more work should be done? How about concerns of academic dishonesty?
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Mark J. Davis, Ph.D
Digital Literacy Teacher
Barrington Public Schools
Barrington, RI
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