Identifying AI-Driven Learning Analytics Performance Improvement Domains in Higher Education Universities

This study supports Higher Education Universities (HEUs) in the United States in their individualized Assessment and Performance Improvement (API) initiatives by identifying key Artificial Intelligence (AI)-driven learning analytics performance improvement domains. Qualitative research was conducted using the Delphi Technique and Framework method. Twenty interviews involving 24 participants from 13 diverse HEUs yielded rich discussions reflecting varied expert perspectives. Three key AI-driven learning analytics performance domains emerged under three value propositions: 1) Economical value and data intelligence, 2) AI-driven decision-making capabilities, and 3) Requirements compliance and outcomes projection. Findings indicate that AI-driven learning analytics performance improvement in HEUs is far more complex than routine data visualization and analytics. The identified domains can guide HEUs in focused API initiatives. Future quantitative studies collecting data from a larger number of HEUs can statistically evaluate the characteristics and relationships identified herein.