Modeling students’ perceptions of artificial intelligence assisted language learning

Abstract

To address the emerging trend of language learning with Artificial Intelligence (AI), this study explored junior and senior high school students’ behavioral intentions to use AI in second language (L2) learning, and the roles of related technological, social, and motivational factors. An eight-factor survey was constructed using a 5-point Likert scale. A total of 524 valid responses were collected, including 280 responses from junior high school students and 244 from senior high school students. The reliability and validity of the scale were satisfactory. The technological and social factors include effort expectancy, performance expectancy, social influence, facilitating conditions of AI-assisted language learning (AILL), which were hypothesized to predict students’ behavioral intention to use AILL with reference to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The motivational factors derived from L2 Motivational Self System theory (i.e. learning experience with AI, cultural interest with AI, and instrumentality-promotion with AI) were hypothesized to be intermediate variables between the technological and social factors and behavioral intention based on the extended UTAUT (UTAUT2). Therefore, UTAUT and the L2 Self System were combined according to UTAUT2 to construct the proposed model in this study, named AILL-Motivation-UTAUT model. The results of the structural equation models of AILL-Motivation-UTAUT showed that performance expectancy, cultural interest, and instrumentality-promotion could predict students’ behavioral intention to use AILL for both junior and senior high students; effort expectancy and social influence could predict behavioral intention to use AILL only for junior high students, learning experience with AI could predict behavioral intention to use AILL only for senior high students, while facilitating conditions could not predict behavioral intention to use AILL for either group. The predictive power (80% for senior high students and 74% for junior high students) of the AILL-Motivation-UTAUT model in this research is higher than or equal to that of UTAUT2 (74%). In addition, this study found that the technological and social factors perceived by students would predict the motivation in AILL. The model verified in this study may inform future studies on AI integration for English as foreign language learning.

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