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

MetaCLASS introduces a framework and benchmark for evaluating LLM-based metacognitive tutoring that explicitly targets self-regulated learning processes (planning, monitoring, debugging, evaluation) through 11 interpretable coach moves, including productive restraint. The paper generates 1,015 annotated tutoring conversations and benchmarks nine LLMs on predicting appropriate metacognitive coaching actions, revealing systematic compulsive intervention bias where models fail to recognize when silence is pedagogically optimal.

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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