专题简介 (Description):

随着学习分析和人工智能在教育中日益普及，数据的收集、使用和共享引发了严峻的伦理与治理挑战。本专题致力于构建负责任的实践框架，核心议题包括学生数据主权、算法透明度、机构问责制以及如何利用数据实现教育公平。征稿欢迎关于具体治理模型、合规技术方案、政策影响评估以及培养学生和教师数据素养的实证研究。目标是汇聚研究，为在数据驱动决策与保护学生权利、促进教育公平之间取得平衡，提供切实可行的指导方针和理论依据。
As learning analytics and AI become pervasive, the collection, use, and sharing of educational data raise critical ethical and governance challenges. This session focuses on building frameworks for responsible practice, addressing student data sovereignty, algorithmic transparency, institutional accountability, and leveraging data for equity. We invite empirical studies on governance models, technical solutions for compliance, policy impact evaluations, and data literacy curricula. The goal is to consolidate research that provides actionable guidance for balancing data-driven decision-making with the protection of student rights and the promotion of educational equity.

征稿主题 (Topics of Interest):
- Student Data Ownership, Privacy, and Informed Consent Models / 学生数据所有权、隐私与知情同意模型
- Algorithmic Fairness and Bias Mitigation in Predictive Analytics / 预测性学习分析中的算法公平性与偏见缓解
- Transparent and Explainable AI for Student Assessment / 用于学生评估的透明与可解释人工智能
- Policy Development for Institutional Learning Data Governance / 机构学习数据治理的政策制定
- Empowering Learners through Personal Data Literacy Education / 通过个人数据素养教育赋能学习者
- Cross-border Educational Data Flow and Compliance / 跨境教育数据流动与合规性
- Ethical Auditing Frameworks for EdTech Platforms / 教育科技平台的伦理审计框架

    

 

Organizer Introduction

章仪，博士，北京师范大学未来教育学院讲师。主要从事教育技术与智能教育研究，关注人工智能、生成式人工智能与虚拟现实等技术在学习与教学中的作用机制与应用模式。研究重点包括智能支持环境中的学习过程分析、同伴反馈与协作学习机制、认知调控与动态干预，以及教师教育与数字素养发展。研究方法上注重多方法整合，综合运用实验研究、学习分析、结构方程模型及多层与元分析，并结合眼动、脑电与近红外等认知神经技术。相关成果发表于 Educational Research Review、Computers & Education 等SSCI期刊。 Yi Zhang, PhD, is a Lecturer at the College of Future Education, Beijing Normal University. Her research focuses on educational technology and intelligent education, with particular attention to the theoretical modeling, cognitive mechanisms, and instructional applications of artificial intelligence, generative AI, and virtual reality in learning and teaching contexts. Her current work examines learning processes and feedback mechanisms in AI-supported environments, including peer feedback, collaborative learning, cognitive regulation, and dynamic instructional interventions, as well as technology-enhanced teacher education and digital competence development. Methodologically, she adopts a multi-method and interdisciplinary approach, integrating experimental designs, learning analytics, structural equation modeling, and multilevel and meta-analytic techniques. She also incorporates cognitive neuroscience methods such as eye-tracking, and electroencephalography (EEG), to investigate how intelligent technologies influence learners’ cognitive processes and behaviors. Her research has been published in SSCI-indexed journals including Educational Research Review, Computers & Education.