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arXiv:2506.04920v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated the ability to generate formative feedback and instructional hints in English, making them inc...
arXiv:2608.22993v1 Announce Type: new Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students u...
arXiv:2605.14322v4 Announce Type: replace Abstract: Language agents are increasingly deployed in professional workflows, yet tutoring remains a high-stakes capability that existing evaluations only p...
RegulaTutor is a theory-informed framework for long‑term personalized tutoring that links a learner’s historical evidence to instructional support. Guided by self‑regulated learning research, it uses a cognitive, affective, metacognitive, and motivational (CAMM) framework to derive evidence‑supported learner interpretations and teaching policies that are updated as new observations arrive. The framework includes PairRoute, an offline refinement procedure that distinguishes harness, model, and mixed failures, proposes targeted harness revisions, and constructs stage‑level supervision for confirmed model failures, improving historical‑evidence accuracy, learner diagnosis, and teaching quality across evaluated model configurations.
arXiv:2608. 03952v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners.
arXiv:2607. 03303v1 Announce Type: new Abstract: While Large Language Models (LLMs) can provide personalized support in learning, several studies have raised concerns regarding their use in education.