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    <abstract>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 interpretable actions aligned to self-regulated learning (SRL) processes. MetaCLASS uses a two-phase procedure that first plans a pedagogical trajectory conditioned on learner profiles (calibration, help-seeking) and then generates natural dialogue consistent with that plan. This process yields 1,015 conversations spanning 7,711 turns, each annotated with turn-level metacognitive labels and supported by comprehensive validation analyses. We benchmark nine open-source LLMs on predicting the next coach move given a question and dialogue context. The best model achieves only 43.2% accuracy in coach move prediction, and models exhibit compulsive intervention bias: in turns where effective metacognitive tutoring requires silence (41.7% of cases), models predict “no intervention” only 6.6% of the time, while severely over-predicting high-intervention moves. These results show that traditional content-based tutoring ability does not translate to metacognitive tutoring competence, positioning MetaCLASS as a testbed for developing tutors that promote SRL.</abstract>
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%0 Conference Proceedings
%T MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support
%A Liu, Naiming
%A Baraniuk, Richard
%A Sonkar, Shashank
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-metaclass
%X 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 interpretable actions aligned to self-regulated learning (SRL) processes. MetaCLASS uses a two-phase procedure that first plans a pedagogical trajectory conditioned on learner profiles (calibration, help-seeking) and then generates natural dialogue consistent with that plan. This process yields 1,015 conversations spanning 7,711 turns, each annotated with turn-level metacognitive labels and supported by comprehensive validation analyses. We benchmark nine open-source LLMs on predicting the next coach move given a question and dialogue context. The best model achieves only 43.2% accuracy in coach move prediction, and models exhibit compulsive intervention bias: in turns where effective metacognitive tutoring requires silence (41.7% of cases), models predict “no intervention” only 6.6% of the time, while severely over-predicting high-intervention moves. These results show that traditional content-based tutoring ability does not translate to metacognitive tutoring competence, positioning MetaCLASS as a testbed for developing tutors that promote SRL.
%U https://aclanthology.org/2026.findings-emnlp.2209/
%P 47312-47340

Markdown (Informal)

[MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support](https://aclanthology.org/2026.findings-emnlp.2209/) (Liu et al., Findings 2026)

• MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support (Liu et al., Findings 2026)

ACL

• Naiming Liu, Richard Baraniuk, and Shashank Sonkar. 2026. MetaCLASS: Metacognitive Coaching for Learning with Adaptive Self-regulation Support. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47312–47340, Budapest, Hungary. Association for Computational Linguistics.