Explainable AI in Education

Dear Colleagues,

This Topic will focus on the development and implementation of explainable artificial intelligence (XAI) techniques within the education sector. This Topic will highlight how XAI can enhance transparency, accountability, and trust in educational technologies. We invite contributions that explore the technical aspects of creating and applying XAI systems for personalized learning, curriculum development, and student assessment. We are particularly interested in research that addresses the technical challenges of integrating XAI in diverse educational settings and studies that illustrate how explainability in AI contributes to more effective educational outcomes. Submissions may include case studies, empirical research, theoretical analyses, or innovative methodologies. This Topic aims to equip educators, developers, and researchers with a deeper understanding of XAI’s role in advancing education, emphasizing the technical foundations and implications of these systems in academic environments. Topics include, but are not limited to, the following:

• XAI for Real-Time Student Feedback
• The Effectiveness of XAI in Personalized Learning Environments
• Technological and Methodological Advances in XAI for Educational Assessments
• XAI for Curriculum Optimization
• XAI in Teacher Decision-Making
• Bias Detection and Mitigation in Educational XAI Applications
• Cross-Cultural Implementations of XAI in Education
• Integration Challenges of XAI in Legacy Educational Systems
• Theoretical Models for Understanding XAI in Education
• Transparent AI Systems for Education

Technical Program Committee Member:
Charanya Ramakrishnan, Macquarie University

Dr. Guanfeng Liu
Dr. Karina Luzia
Dr. Luke Bozzetto
Dr. Tommy Yuan
Prof. Dr. Pengpeng Zhao
Topic Editors