Developing and Validating a University Language Teacher Belief Scale for AI-Assisted Instruction

Résumé fourni par la source

Teachers’ beliefs play a critical role in shaping how AI tools are interpreted and used in instruction, yet these beliefs have rarely been systematically assessed in university language teaching. This study aims to develop and validate a measurement instrument for assessing teachers’ beliefs about AI-assisted instruction, grounded in the Intelligent-Technological Pedagogical and Content Knowledge framework. A sequential, two-wave quantitative design was employed, with data from 508 teachers used for exploratory factor analysis (EFA), followed by confirmatory validation using an independent sample of 487 teachers. Data analyses were conducted using SPSS for EFA, AMOS for confirmatory factor analysis and Mplus for measurement invariance testing. The results supported a reliable five-factor belief structure: AI knowledge beliefs, AI content beliefs, AI pedagogy beliefs, AI integration beliefs and AI ethics beliefs. The scale demonstrated satisfactory reliability, construct validity and stability. Additionally, measurement invariance results indicated that the scale functioned equivalently across gender, teaching experience and educational background. This study provides a psychometrically robust tool for future empirical inquiry and enhances understanding of language teacher beliefs in AI-assisted instruction.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.