When AI Works: Personalized Learning Shows Real Promise
At the University of Maryland, Professor Charles Harry represents a growing cohort of educators who've learned to leverage AI effectively. Harry, director of the Center for Governance of Technology and Systems, uses AI extensively in course design and actively encourages student use—with guardrails. His students, some with no coding background, can now manipulate complex data and build spatial maps in just 13 weeks, tasks that would have been impossible in a traditional Programming 101 course. A 2024 meta-analysis of 36 studies found moderately positive effects from AI-assisted personalized learning, with the strongest gains in knowledge retention and overall competence.
Why K-12 Educators Should Care: Harry's approach offers a blueprint for K-12 implementation: mandate AI use thoughtfully, design more complex assignments that AI enables rather than completes, and focus on whether students' core ideas are their own. He saves 5-6 hours per week using AI tools—time he redirects toward better teaching. The key is reimagining pedagogy: "We have to reimagine how we're teaching and how we leverage these tools," Harry explains. AI can provide real-time support that transforms when and how students get help—imagine homework assistance available at midnight, or instant feedback before a paper is submitted rather than after.
Key Takeaway: AI's potential for personalization is real, but it requires intentional design and clear boundaries. The goal isn't to make learning easier but to make it more effective. Harry's warning is crucial: students must stop treating AI like "an easy button" and start using it to unlock deeper learning.