Exploring the Relationship Among Motivation, Self-Efficacy, and Behavioral Intention to Use Artificial Intelligence in English Academic Writing: Evidence from Under-Resourced Postgraduates

Currently, artificial intelligence (AI) has emerged as a transformative force in language learning and teaching, attracting significant attention. The application of AI has been shown to have positive impacts on various aspects of second language (L2) education. However, in the context of AI’s deep integration into English academic writing, how psychological factors of L2 learners influence their behavioral intention (BI) to use AI tools remains an area that requires further exploration. This study, based on the L2 Motivational Self System (L2MSS) and Control-Value Theory (CVT), aims to investigate the relationship among L2 learners’ motivation (including ideal L2 writing self, ought-to L2 writing self, and writing learning experience), writing self-efficacy, and BI to use AI in English academic writing. We distributed questionnaires to 309 postgraduates from five universities in under-resourced regions of China, among which 307 responses were valid. Subsequently, we analyzed the data using SPSS 27.0 and AMOS 26.0. The findings indicate that in English academic writing, the postgraduates’ ideal L2 writing self, ought-to L2 writing self, and writing learning experience all positively predict their BI to use AI, and their writing self-efficacy plays a partial mediating role between these three motivational factors and the BI to use AI. Theoretically, this study demonstrates that even in the era of rapid technological development, CVT and L2MSS remain valuable for research related to L2. Practically, this study shows that in English academic writing instruction, educators should focus on enhancing learners’ motivation and self-efficacy to encourage their more active use of AI tools.