Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education and demonstrate that transparency considerations are embedded in the entire AI development process from the data collection stage until the AI system is deployed in the real world and iteratively improved. We also demonstrate how transparency enables the implementation of other ethical AI dimensions in Education like interpretability, accountability, and safety. In conclusion, we discuss the directions for future research in this newly emerging field. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.
Center for Research on Foundation Models (CRFM)（基础模型研究中心（CRFM））；Stanford Institute for Human-Centered Artificial Intelligence (HAI)（斯坦福以人为本人工智能研究所（HAI））；Stanford University（斯坦福大学）
The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges
University of Alberta（阿尔伯塔大学）；Federation of American Scientists（美国科学家联合会）；Educational Testing Service（教育考试服务中心）；University of Maryland（马里兰大学）；University of Iowa（爱荷华大学）；Cambium Assessment（坎比姆评估公司）；Washington State University（华盛顿州立大学）；Acuity Insights（敏锐洞察公司）；PSI Services（PSI服务公司）；University of Delaware（特拉华大学）；Concordia University of Edmonton（埃德蒙顿康考迪亚大学）
The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems
University of Cambridge（剑桥大学）；University of Washington（华盛顿大学）；Harvard Law School（哈佛法学院）；Stanford University（斯坦福大学）；Concordia AI（康科迪亚AI）；University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；Hebrew University of Jerusalem（耶路撒冷希伯来大学）
Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications
ByWater Institute, Tulane University（杜兰大学拜沃特研究所）；Office of Teaching, Learning, and Technology, University of Iowa（爱荷华大学教学、学习与技术办公室）；River-Coastal Science and Engineering, Tulane University（杜兰大学河流与海岸科学与工程系）
本研究评估了在R1公立大学工程本科课程中应用的AI学习框架Educational AI Hub，通过混合方法发现学生认可其便利性但存在伦理顾虑，强调需提升可用性、伦理透明度并辅以教师指导以促进有效AI学习参与。
