MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support

MetaCLASS introduces a benchmark for evaluating LLM-based metacognitive tutoring systems that support self-regulated learning through 11 interpretable coach moves (planning, monitoring, debugging, evaluation), including strategic restraint. The framework generates 1,015 annotated tutoring conversations and tests 9 LLMs on predicting pedagogically appropriate metacognitive moves, revealing systematic over-intervention bias where models fail to recognize when silence promotes productive struggle.

Large language models can generate fluent explanations, but effective tutoring requires supporting the learner's thought process, not just delivering content. Metacognitive tutoring targets this gap by prompting planning, monitoring, debugging, and evaluation, and crucially, deciding when to be active versus minimally present, based on learner signals and trajectory. We introduce MetaCLASS, a learning-science grounded framework that formulates metacognitive tutoring as move selection over 11 int

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