Designing AI Education Ecosystem Indicators in Higher Education: A Delphi and AHP Approach

• In Event: AERA Roundtable Session Wednesday 1:45 pm - Gold 1
  In Roundtable Session: Virtual Communication in Higher Education

Abstract

This study constructs an evaluation indicator system for the artificial intelligence (AI) educational ecosystem in higher education institutions. Grounded in ecosystem theory and aligned with the needs of AI integration in universities, it combines the Delphi method and Analytic Hierarchy Process (AHP). Two Delphi rounds with 22 experts ensured indicator reliability, while AHP weighting by 10 experts identified the relative importance of each dimension. The final framework includes five core dimensions: policy guidance and regulation (0.27), technological and environmental infrastructure (0.31), teacher competence and practice (0.18), student competence and development (0.16), and development and feedback (0.08). The resulting framework provides a validated tool to guide institutional planning, support leadership decisions, and enhance adaptive capacity for AI integration in higher education.

Authors

• Xinyi Wu, Shanghai Jiao Tong University

• Qinglin Zhang, Shanghai Jiao Tong University

• Feng-Kuang Chiang, Shanghai Jiao Tong University