RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning
[Sztuczna inteligencja] Autorzy: Yonggang Li, Boqian Han, Rui Sheng i 2 więcej
Long-term personalized tutoring requires language models to translate a learner's evolving history into appropriate instructional support. Existing LLM-based tutoring systems use memory and retrieval to personalize responses, but the pedagogical rationale linking historical evidence to teaching strategies is not always explicit. We introduce RegulaTutor, a theory-informed framework for long-term personalized tutoring that connects historical evidence, learner interpretation, and learner-facing instruction. Guided by self-regulated learning (SRL) research, its tutoring harness operationalizes a cognitive, affective, metacognitive, and motivational (CAMM) framework for deriving evidence-supported learner interpretations and teaching policies. The resulting policies retain links to their supporting evidence and are updated as new observations arrive. This staged workflow also supports syst…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja