DEVELOPMENT AND EFFECTIVENESS EVALUATION OF AN AI-ENHANCED BLENDED LEARNING MODEL FOR ENHANCING PROFESSIONAL TEACHING COMPETENCIES OF PRE-SERVICE TEACHERS AT PHINYABUNDIT COLLEGE

Abstract

      This study aimed to: (1) analyze the current conditions, problems, and needs in learning management among pre-service teachers; (2) develop an AI-Enhanced Blended Learning Model (AIBL Model); and (3) evaluate its effectiveness in enhancing professional teaching competencies, learning achievement, and student satisfaction. This research employed a Research and Development (R&D) approach using a census sample of 150 pre-service teachers at Phinyabundit College. Research instruments included a needs assessment questionnaire, AIBL-based instructional plans, a professional competency assessment form, an achievement test, and a satisfaction questionnaire. All instruments demonstrated acceptable content validity (IOC = 0.67–1.00) and reliability coefficients above .80. Data were analyzed using mean, standard deviation, dependent t-test, and effect size (Cohen’s d).
      The findings indicated that the current state of learning management was at a moderate level, while the need for improvement - particularly in AI integration - was high. The developed AIBL model demonstrated the highest level of appropriateness ( = 4.68, SD = 0.41). After implementation, students’ professional teaching competencies significantly improved (t = 18.76, p < .001, d = 1.52), as did their learning achievement (t = 21.35, p < .001, d = 1.78), indicating very large effect sizes. Additionally, overall student satisfaction was at the highest level ( = 4.56, SD = 0.44). These findings provide strong empirical evidence that the AIBL model is highly effective in enhancing professional teaching competencies and holds practical implications for teacher education in the digital era.