Development of self-efficacy scales for AI and metaverse in music learning and teaching

Abstract

Artificial intelligence (AI) and metaverse technologies have significantly transformed music education by enabling personalized learning, interactive instruction, digital content creation, and immersive virtual learning environments. Despite their increasing adoption, there is a lack of valid and reliable instruments specifically designed to assess the self-efficacy of learners and educators regarding the use of these technologies in music education. Therefore, this study aimed to develop four self-efficacy scales and examine their psychometric properties. The study employed a scale development research design. Accordingly, the Artificial Intelligence in Music Learning Self-Efficacy Scale (AIMLSES), the Artificial Intelligence in Music Teaching Self-Efficacy Scale (AIMTSES), the Metaverse-Based Music Learning Scale (MBMLS), and the Metaverse-Based Music Teaching Scale (MBMTS) were developed. Item pools were generated based on the relevant literature, content validity was established through expert evaluations, and second-order confirmatory factor analyses were conducted using data collected from different participant groups. Construct validity was examined using the Diagonally Weighted Least Squares (DWLS) estimation method. Convergent validity was evaluated through Average Variance Extracted (AVE), whereas reliability was assessed using Cronbach’s alpha and McDonald’s omega coefficients. The findings confirmed the proposed second-order factor structures for all four scales and demonstrated satisfactory model fit indices, high convergent validity, and excellent internal consistency reliability. Overall, the results indicate that the developed instruments are valid and reliable measures for assessing self-efficacy regarding the use of artificial intelligence and metaverse technologies in music learning and teaching. The scales provide comprehensive assessment tools that can support future research on technology integration, digital transformation, and innovative instructional practices in music education.

Keywords

• Metaverse-based music learning
• Metaverse-based music teaching
• Music learning self-efficacy
• Music teaching self-efficacy
• Scale development
• AI in music learning
• AI in music teaching

Ethical Statement

This study was conducted with the approval of the Research Ethics Committee of International Vision University. Before data collection, participants were informed about the purpose of the study, and voluntary participation was ensured. The data obtained from the participants were used only for scientific purposes, personal information was kept confidential, and the principles of scientific research and publication ethics were followed throughout the study.

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