Self-Regulated Learning

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8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning
Oct 9, 2026Yonggang Li, Boqian Han, Rui Sheng +2Self-Regulated Learning Intelligent Tutoring Systems
The Hong Kong University of Science and Technology · The Chinese University of Hong Kong
AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective
Jun 22, 2026Boxuan Ma, Huiyong Li, Gen Li +3Self-Regulated Learning Programming Education
Faculty of Arts and Science, Kyushu University, Fukuoka, Japan. · Research Institute for Information Technology, Kyushu University, Fukuoka, Japan. · Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan. +1
Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics
May 31, 2026Mohammad Amanlou, Yasaman Amou-Jafari, Mehrad Livian +3Self-Regulated Learning AI in Education
aSchool of Electrical and Computer Engineering, University of Tehran, Iran · bTehran Institute for Advanced Studies, Khatam University, Iran
Self-Regulated Learning in Essay Writing: Consistency of Strategies and Impact on Outcomes
May 14, 2026Gloria Fernández-Nieto, Kiyoshige Garcés, Mladen Raković +4Self-Regulated Learning Online Learning
Department of Data Science & AI Monash University Australia, Victoria
AI-Assisted Code Review as a Scaffold for Code Quality and Self-Regulated Learning: An Experience Report
Apr 25, 2026Eduardo Oliveira, Michael Fu, Patanamon Thongtanunam +2Self-Regulated Learning Generative AI in Education
The University of Melbourne Melbourne, Australia · University of Eastern Finland Joensuu, Finland
Cross-Course Generalizability of SRL-Aligned Predictive Models Using Digital Learning Traces
Apr 14, 2026Jakob Schwerter, Loreen Sabel, Judith Bose +5Self-Regulated Learning Cross-Domain Generalization
Hector Research Institute of Education Sciences and Psychology, University of Tübingen · TU Dortmund University · University of North-Carolina, Chapel Hill +2
Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions
Feb 24, 2026Paras Sharma, YuePing Sha, Janet Shufor Bih Epse Fofang +6Self-Regulated Learning Educational Dialogue Systems
University of Pittsburgh Pittsburgh, Pennsylvania, USA · Carnegie Mellon University Pittsburgh, Pennsylvania, USA · Bowie State University Bowie, Maryland, USA
Self-Regulated Reading with AI Support: An Eight-Week Study with Students
Feb 10, 2026Yue Fu, Joel Wester, Niels Van Berkel +1Self-Regulated Learning AI in Education
University of Washington, USA · University of Copenhagen, Denmark · Aalborg University, Denmark

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RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning

Yonggang Li, Boqian Han, Rui Sheng, Yushi Sun+1

The Hong Kong University of Science and Technology · The Chinese University of Hong Kong

Self-Regulated Learning Intelligent Tutoring Systems AI in Education Personalized Language Models

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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.