UNIVERSITY LECTURERS' ARTIFICIAL INTELLIGENCE CURRICULUM INTEGRATION INTENTIONS: THEORY OF PLANNED BEHAVIOR EVIDENCE FROM ASIA

Artificial intelligence (AI) is transforming business practices and increasing the need for higher education institutions to integrate AI-related competencies into business curricula. However, empirical evidence on the behavioral factors influencing university lecturers’ intentions to integrate AI, particularly in Asian higher education, remains limited. Grounded in the Theory of Planned Behavior (TPB), this study examines the determinants of lecturers’ intentions to integrate AI into business curricula. A cross-sectional survey was conducted with 235 university lecturers from several Asian countries, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The model examines the effects of attitude, subjective norms, and self-efficacy while incorporating AI-related education and prior AI use as background factors. The findings show that self-efficacy is the strongest predictor of AI integration intention, followed by attitude, whereas subjective norms have no significant effect. AI-related education indirectly strengthens intention through attitude and self-efficacy, while prior AI use primarily enhances perceived capability. This study suggests that competency development and AI-focused faculty training may be more important than normative pressure in promoting AI curriculum integration among lecturers in the sampled higher education contexts.