RegulaTutor：一种用于长期个性化学习的理论驱动框架
RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning
AI总结：
RegulaTutor是一种理论驱动的长期个性化辅导框架，结合SRL与CAMM框架，通过PairRoute优化，提升了历史证据准确性等，助力终身个性化教育。
摘要：
长期个性化辅导要求语言模型将学习者不断变化的历史转化为合适的教学支持。现有的基于大语言模型（LLM）的辅导系统使用记忆和检索来个性化响应，但将历史证据与教学策略关联的教学原理并不总是明确的。我们提出RegulaTutor，这是一种用于长期个性化辅导的理论驱动框架，它将历史证据、学习者解读和面向学习者的教学联系起来。在自我调节学习（SRL）研究的指导下，其辅导工具利用认知、情感、元认知和动机（CAMM）框架来推导有证据支持的学习者解读和教学策略。生成的策略保留与其支持证据的关联，并随着新观察结果的到来而更新。这种分阶段工作流程还通过PairRoute支持系统的优化，PairRoute是一种使用中间执行记录来区分工具、模型和混合故障的离线程序。PairRoute提出并评估针对性的工具修订，然后为在选定工具下仍存在的已确认模型故障构建经过审查的阶段级监督。我们将这种优化重点放在历史证据获取上，这是个性化教学的基础，允许将输出与学习记录和参考答案进行核对。RegulaTutor在评估的模型配置中，平均提高了历史证据准确性、学习者诊断和教学质量判断。PairRoute的结果显示，在优化轨迹上，独立模型的历史证据准确性最终得到了提升。这些结果共同凸显了RegulaTutor的潜力，即通过随学习者历史演变的教学策略和对辅导系统的针对性改进，支持终身个性化教育。
英文摘要：
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 systematic refinement through PairRoute, an offline procedure that uses intermediate execution records to distinguish harness, model, and mixed failures. PairRoute proposes and evaluates targeted harness revisions, then constructs reviewed stage-level supervision for confirmed model failures remaining under the selected harness. We focus this refinement on historical evidence acquisition, which underpins personalized instruction and allows outputs to be checked against learning records and reference answers. RegulaTutor improves historical-evidence accuracy, learner diagnosis, and judged teaching quality on average across evaluated model configurations. PairRoute results show an endpoint improvement in standalone-model historical-evidence accuracy over the refinement trajectory. Together, these results highlight RegulaTutor's potential to support lifelong personalized education through instructional policies that evolve with learner histories and targeted improvements to the tutoring system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
