A Model for Enhancing AI-Driven Active Learning Competencies of Private University Lecturers in the Digital Era

Abstract

This article aimed 1) to study the components and indicators for enhancing AI-driven active learning competencies; 2) to develop a model for such enhancement; and 3) to propose guidelines for enhancing AI-driven active learning competencies for private university lecturers in the digital era. This study employed a mixed-methods research design, incorporating both qualitative and quantitative approaches. The research sample consisted of 380 full-time private university lecturers, selected through multi-stage random sampling. Three types of research instruments were used: 1) semi-structured interviews, 2) 5-point Likert scale questionnaires, and 3) focus group discussion topics. Quantitative data were analyzed using percentages, means, standard deviation, and Confirmatory Factor Analysis (CFA), while qualitative data underwent content analysis. The research findings are as follows: 1) The model for enhancing AI-driven active learning competencies of private university lecturers in the digital era consists of three main components with a total of 21 indicators. 2) The developed model demonstrated an excellent fit with empirical data. 3) Guidelines for enhancing AI-driven active learning competencies consist of three main strategies. This research contributes systematic knowledge regarding the structure of active teaching competencies that integrate artificial intelligence ethically. It serves as a conceptual framework and policy tool for developing private university faculty, as well as a guideline for designing training curricula to sustainably elevate the quality of instruction in the digital age.