Class Disrupted AI in K–12: Feedback, Curiosity, and the New Frontier of Teaching
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Dec 8, 2025 Laurence Holt, a senior advisor at XQ Institute and The Teaching Lab, dives into the evolving role of AI in K–12 education. He highlights the three main use cases: generating materials, providing feedback, and AI tutoring. The discussion distinguishes between feedback and grading, emphasizing the need for revision in learning. Holt also critiques the limitations of current AI tutoring, the pitfalls of generic chatbots, and the importance of fostering curiosity in classrooms. They touch on the barriers to effective edtech and the necessity for tools that encourage social learning.
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Feedback Is The Most Mature Application
- AI feedback can match a median teacher and scale timely responses students rarely get.
- Holt suggests universal, high-quality feedback K–12 could be transformational.
Prioritize Revision Over Final Grades
- Avoid conflating grading with feedback because grades can undermine learning.
- Provide iterative opportunities to revise work so students engage with feedback and learn.
Vertical Tools Need Instructional Context
- Horizontal chatbots (ChatGPT-style) differ from vertical, context-aware tools teachers need.
- Effective classroom AI must understand instructional context and student data, which current tools lack.
