The Impact of Artificial Intelligence (AI) on the Development of Educational Quality in Private Universities In Bangkok, Thailand
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Abstract
This study aimed to (1) examine the effects of artificial intelligence (AI) adoption in teaching and learning, administrative management, and personnel capability on the effectiveness of AI-driven learning and management processes in private universities in Bangkok, Thailand; (2) investigate the influence of AI-driven process effectiveness on the quality of teaching and learning; and (3) propose policy and practical guidelines for the sustainable integration of AI in higher education. A mixed-methods research design was employed, combining quantitative and qualitative approaches. The quantitative phase involved a sample of 510 administrators and academic personnel from private universities in Bangkok, Thailand. Data were collected using a structured questionnaire that was assessed for content validity and reliability. In-depth interviews were conducted to enrich and support the interpretation of the quantitative findings. Quantitative data were analyzed using descriptive statistics, Pearson’s correlation analysis, Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM).
The results indicated that AI adoption in teaching and learning, AI utilization in administrative management, and personnel capability had significant positive effects on the effectiveness of AI-driven learning and management processes. Furthermore, process effectiveness significantly enhanced students’ learning experiences, institutional management effectiveness, and the overall quality of teaching and learning. However, institutional management effectiveness did not exert a direct significant influence on educational quality. The structural model demonstrated an excellent fit with the empirical data. The findings suggest that sustainable improvement in educational quality requires systematic integration of AI into pedagogical practices, continuous development of personnel competencies, and coherent governance frameworks. Administrative efficiency alone is insufficient to achieve meaningful educational quality enhancement. These findings provide valuable implications for policymakers and university administrators seeking to leverage AI for sustainable development in higher education.
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