Institutional Training on Learning Analytics and Teacher Adaptation to Personalized Artificial Intelligence Learning in a Normal University in China

Abstract

In the context of rapid digital transformation in Chinese higher education, the integration of Personalized Artificial Intelligence Learning (PAIL) and Learning Analytics (LA) is reshaping instructional roles. Grounded in the Sociotechnical Systems Theory for AI in Education (STSF-AI), this study investigates the relationship between institutional training on learning analytics and teacher adaptation to personalized AI learning among faculty members at Minnan Normal University, China. Employed is a descriptive-comparative-correlational research design​ using a researcher-made questionnaire with stratified random sampling. The study measures two core constructs: (1) self-assessment of institutional LA training across five dimensions—data interpretation competency, platform feature mastery, ethical data usage, instructional intervention design, and actionable insight formulation; and (2) teacher adaptation to AI across five indicators—AI tool integration proficiency, personalized pathway differentiation, AI-driven insight application, adaptive feedback cycle management, and balance of automated vs. human instruction. Descriptive statistics (frequency, percentage, weighted mean), One-Way ANOVA with Scheffe post-hoc, and Pearson Product-Moment Correlation (r) were used to analyze demographic differences and variable relationships at p < 0.05. Findings are expected to reveal how data literacy training scaffolds pedagogical agency in AI-enabled classrooms. As an output, the study culminates in a proposed AI-Enhanced Professional Learning Community Program​ to help administrators balance algorithmic efficiency with human-centered teaching. The results contribute localized empirical data to the discourse on AI integration and teacher professional development in East Asian normal universities.