Exploring Key Issues in Learning Data for AI Digital Textbooks

• In Event: AERA Roundtable Session Friday 3:45 pm - Gold 2
  In Roundtable Session: Capacity and Infrastructure for Responsible AI in Education

Abstract

As digital learning expands, Korea’s AI digital textbooks present new challenges in standardizing learning data. This study explores key issues in the collection, analysis, and utilization of learning data in Korean public education. Using a process-based analytical framework, we conducted literature and case reviews and identified core issues, such as inconsistent authority among stakeholders, lack of national standards, limited interoperability, and absence of ethical guidelines. By examining the implementation of global standards (e.g., xAPI and Caliper) and national cases such as Japan’s LEAF and Korea’s Hi-Learning platform, we discuss directions for building a sustainable learning data ecosystem. Korea’s case provides applicable insights for countries seeking to pursue data-driven educational innovation in ways that align with their educational context.

Authors

• Young Ri Lee, Seoul National University

• Seonmin Eun, Seoul National University

• Anna Kim, Seoul National University

• Young Hoan Cho, Seoul National University