研究
我研究AI 原生学习系统：AI 如何重塑教学、学习与教育关系。
了解团队的研究、项目与学术论文。
研究框架
四个相互关联的层次（点击跳转）：
1. 教育哲学与伦理
AI 如何重塑学习、能动性与教育？我们研究学生、教师、AI 系统与环境如何共同构成学习过程，关注人的能动性、教学能动性、认识能动性、分布式认知，以及过度优化的伦理风险。
2. AI 原生学习基础设施
如何将学习系统设计成整合的 AI 原生环境？我们关注多智能体系统、课堂编排平台、协作环境与 AI 智能体创作系统，以支持教学、学习和实时干预。
3. 学习分析与解释
如何让课堂过程可见、可解释？我们整合课堂话语、音视频、交互日志、教师备课与学生评价数据，在预测之外重视解释，支持教师反思与教学决策。
4. 学习与教学成效
AI 支持的系统会带来哪些影响？我们研究学生的 AI 素养、计算思维、问题解决与认识能动性，以及教师的能动性、设计能力和专业成长，同时关注课堂实践的变化。
研究主题
六个相互关联的主题。点击节点跳转：
教育中的人机共同配置
当人类与 AI 共同构成学习系统时，教育过程如何重新组织？这一主题连接人机组装体、教师与 AI 共创，以及学生通过设计、测试和改进 AI 智能体开展学习的实践，关注分布式能动性、教学能动性与认识能动性。
相关工作
· Teachers' Behavior in Building Agents Based on Hierarchical Clustering and Thematic Analysis (AERA 2026)
· Empowering Teachers as Creators of Pedagogical Agents: An Integrated Perspective of Constructionism, ICAP, and TPACK (AERA 2026)
· An Activity-Theoretical Approach to Teacher Professional Development in Pedagogical AI Agent Design (ISLS, ICLS 2026)
· Modeling AI-TPACK in Practice: Insights from Teachers' Multi-Agent Workflow Design (ISLS, ICLS 2026)
· Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study (AIED 2026)
多智能体学习系统
相互协调的 AI 智能体如何支持教与学？这项工作将多智能体系统作为教育基础设施，探索智能体编排、师生与 AI 的协同，以及课堂中的系统层面智能。
相关工作
· MIRACLE: Multi-Agent Intelligent Regulation to Advance Collaborative Learning Environment (ISLS, CSCL 2026) — Best Short Paper Award
· Hierarchical Multi-Agent System for Instructional Design in Music Knowledge Building (ISLS, CSCL 2026)
· A Multi-Agent System (MAS)-Based Tool to Support Novice Teachers in Knowledge Building Pedagogy (ISLS, CSCL 2025)
协作学习与知识建构
AI 如何支持集体认知与社会共享调节？我们关注 CocoNote 等协作环境，研究小组认知、知识建构过程与学习的社会共享调节。
相关工作
· CocoNote Supported Project-Based Learning Environment: Perspectives of Construction and Collaboration (ISLS, CSCL 2024)
· CocoNote: Agents-aided Collaborative Learning Environment Enhances Socially Shared Regulation (ISLS, CSCL 2025) 🏆
· MIRACLE: Multi-Agent Intelligent Regulation to Advance Collaborative Learning Environment (ISLS, CSCL 2026) — Best Short Paper Award
多模态学习分析
多模态数据如何帮助理解课堂过程？我们探索课堂话语分析，整合音频、视频、交互日志和评价数据，并为教师反思和决策提供分析支持。
相关工作
· Teachers' Behavior in Building Agents Based on Hierarchical Clustering and Thematic Analysis (AERA 2026)
· Modeling AI-TPACK in Practice: Insights from Teachers' Multi-Agent Workflow Design (ISLS, ICLS 2026)
进行中
· 整合音频、视频、交互日志与评价数据
· 通过可解释的分析支持教师反思
AI 中介学习中的学生发展
参与 AI 中介的学习环境如何影响学生发展？我们考察设计、测试、调试与改进的循环如何支持计算思维、自我效能感与认识能动性，同时关注学习者之间的差异及非线性发展。
相关工作
· Computational Thinking Development in AI Agent Creation: A Mixed-Methods Study (AIED 2026)
适应性与合乎伦理的人机学习设计
AI 如何在支持学习的同时保留学习者的能动性？我们从传统适应性学习进一步拓展到认识能动性、人机交互设计及其伦理影响。
进行中
· 设计能够拓展学习者可能性的系统
· 在个性化与开放性之间取得平衡
· 理解 AI 如何重塑学习轨迹
代表论文
Sun, Y., Xin, H., Niu, Q., Li, S., Huang, L., & Chen, G. (2026, June). Computational thinking development in AI agent creation: A mixed-methods study [Short paper]. Proceedings of the 2026 International Conference on Artificial Intelligence in Education (AIED), Seoul, Republic of Korea.
Xin, H., Niu, Q., Li, S., Sun, Y., Chai, C., Huang, L., & Chen, G. (2026, June). An activity-theoretical approach to teacher professional development in pedagogical AI agent design [Long paper]. Proceedings of the 2026 ISLS Annual Meeting, Irvine, CA.
Sun, Y., Xin, H., Li, S., Niu, Q., Chai, C., Huang, L., & Chen, G. (2026, June). Modeling AI-TPACK in practice: Insights from teachers' multi-agent workflow design [Short paper]. Proceedings of the 2026 ISLS Annual Meeting, Irvine, CA.
Li, S., Xin, H., Sun, Y., Niu, Q., Huang, L., Chen, G., Zhang, Y., & Chai, C. S. (2026). MIRACLE: Multi-agent intelligent regulation to advance collaborative learning environment. In C. Rosé, G. Gweon, C. Asterhan, & A. Keune (Eds.), Proceedings of the 19th International Conference on Computer-Supported Collaborative Learning — CSCL 2026 (pp. 524–528). International Society of the Learning Sciences. Best Short Paper Award — CSCL 2026.阅读论文
Zhang, M., Zhu, M., Xin, H., & Zhang, Y. (2026, June). Hierarchical multi-agent system for instructional design in music knowledge building [Poster]. Proceedings of the 2026 ISLS Annual Meeting, Irvine, CA.
Xin, H., Yu, Y., Li, S., Niu, Q., Gao, L., Huang, L., & Chai, C. (2026, April). Teachers' behavior in building agents based on hierarchical clustering and thematic analysis [Roundtable]. 2026 AERA Annual Meeting, Los Angeles, CA.
Li, S., Xin, H., Yu, Y., Niu, Q., Gao, L., Huang, L., & Chai, C. (2026, April). Empowering teachers as creators of pedagogical agents: An integrated perspective of constructionism, ICAP, and TPACK [Poster]. 2026 AERA Annual Meeting, Los Angeles, CA.
Xin, H., Li, S., Huang, L., Yip, V. W. Y., Niu, Q., Chen, X., & Liu, J. (2025). CocoNote: Agents-aided collaborative learning environment enhances socially shared regulation [Short paper]. Proceedings of CSCL 2025. 🏆 Outstanding Short Paper Award
Xin, H., Lan, L., Niu, Q., Hu, Z., & Zhang, Y. (2025). A multi-agent system (MAS)-based tool to support novice teachers in knowledge building pedagogy. Proceedings of CSCL 2025.
Xin, H., Niu, Q., Lan, L., Xiao, Z., & Wu, F. (2024). CocoNote supported project-based learning environment: Perspectives of construction and collaboration [Poster]. Proceedings of CSCL 2024.
Zhong, X., Xin, H., Li, W., Zhan, Z., & Cheng, M. (2024). The design and application of RAG-based conversational agents for collaborative problem solving. Proceedings of the 2024 9th International Conference on Distance Education and Learning.