Developing and testing a measurement of behavioral intention towards artificial intelligence in EFL/ESL learning: taking individual differences into the equation

The integration of Artificial Intelligence (AI) into education holds significant potential, yet understanding students’ behavioral intentions toward AI, particularly in relation to individual differences, remains underexplored. This study addresses this gap by developing and validating a measurement scale for Chinese university students’ behavioral intentions toward AI in English as a foreign/second language (EFL/ESL) learning by integrating the Technology Readiness and Acceptance Model (TRAM) to account for individual differences. The scale includes four individual factors—optimism, innovativeness, discomfort, and insecurity—alongside traditional TAM constructs such as perceived ease of use, perceived usefulness, and behavioral intention. Data were collected from 401 Chinese university students, and the scale’s reliability and validity were tested using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Results indicate that the scale is both reliable and valid, with strong factor loadings and model fit indices. Theoretical and pedagogical implications for EFL/ESL language learning are discussed.