AI-ENHANCED LEARNING MEDIA FOR BUDDHIST MEDITATION EDUCATION: A DEVELOPMENT STUDY IN HIGHER EDUCATION

Abstract

The rapid development of artificial intelligence (AI) has created new opportunities for higher education, particularly in the development of multimedia learning resources that can provide more flexible, accessible, and engaging learning experiences. However, the application of AI-generated learning media in Buddhist religious education, particularly Buddhist meditation education, remains relatively underexplored. This study aimed to develop and evaluate an AI-based learning medium for the Samadhi course, with particular emphasis on introductory Buddhist meditation material. A research and development approach based on the ADDIE framework was employed, consisting of analysis, design, development, implementation, and evaluation. The development process integrated several AI-supported tools for generating instructional text, visual assets, and presenter-based video content, followed by conventional video editing and integration. Data were collected through observation, interviews, expert evaluation, and student response questionnaires. The developed product was subsequently evaluated through individual, small-group, and field trials. The field implementation involved 12 students and produced an overall score of 89.35%, categorized in the original study as “very good” and “highly feasible.” Individual testing identified weaknesses primarily in language style and audio/music balance, which were subsequently revised. Small-group and field evaluations indicated improved perceptions of comprehensibility, visual quality, audio quality, and instructional usefulness. The findings suggest that AI can function not merely as a content-generation technology but as a component of an instructional design process for developing contextual multimedia learning resources in Buddhist higher education. Nevertheless, the study evaluates primarily perceived feasibility and usability rather than learning gains. Future research should therefore employ larger samples, comparative or quasi-experimental designs, and direct measures of learning outcomes.