ECNUClaw: A Learner-Profiled Intelligent Study Companion Framework for K-12 Personalized Education
2026-05-08 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors developed ECNUClaw, a free tool to help create smart study partners for K-12 students. It tracks five types of student traits—like thinking skills and feelings—by analyzing conversations with the study partner. Using these traits, it adjusts how it helps students in real time, such as giving more encouragement or changing question difficulty. The system is based on ideas from Chinese education research and works with multiple Chinese language AI models. The authors share details about how it works and have made the code publicly available.
learner profileK-12 educationadaptive learningmetacognitionBloom's taxonomylarge language modelseducational technologystudent modelingintelligent tutoring systemsChinese educational frameworks
Authors
Yizhou Zhou, Jiayin Li, Zhi Zhang
Abstract
We introduce ECNUClaw, an open-source framework for building learner-profiled intelligent study companions in K-12 education. The system constructs and maintains a five-dimension learner profile -- covering cognitive, behavioral, emotional, metacognitive, and contextual dimensions -- by extracting signals from student-companion dialogues at each turn. Profile updates feed directly into an adaptive strategy engine that adjusts the companion's guidance intensity, encouragement frequency, and Bloom's taxonomy scaffolding in real time. The framework design draws on three theoretical strands from the Chinese educational technology literature: Zhang's Digital Portrait Three-Layer Framework for learner assessment, the Education Brain model for educational system architecture, and the Human-AI Collaborative IQ concept for companion design philosophy. ECNUClaw is implemented in Python and supports seven Chinese LLM providers through a unified OpenAI-compatible adapter layer. We describe the system architecture, the profiling and adaptation mechanisms, and discuss limitations and next steps. The source code is available at https://github.com/bushushu2333/ECNUClaw.