Volume 4,Issue 5
Research on Enhancing College Students’ English Writing Ability Driven by Multimodal Generative AI
This study investigates the relationships among Multimodal Generative AI Usage Frequency (MGAIUF), Multimodal Generative AI Function Perception (MGAIFP), English Writing Learning Engagement (EWLE), English Writing Confidence and Attitude (EWCA), and English Writing Ability (EWA). Grounded in Multimodal Learning Theory (MLT), Student Engagement Theory (SET), and Self-Determination Theory (SDT), this study employed a quantitative survey design by means of a self-developed questionnaire. Data were collected from 221 Chinese college students with experience using generative AI (GenAI) tools for English writing and analyzed using SPSS 25.0 and AMOS 24.0 through reliability testing, confirmatory factor analysis (CFA), and structural equation modeling (SEM). The results indicate that both MGAIUF and MGAIFP significantly and positively affect EWLE and EWCA. Moreover, EWLE and EWCA significantly enhance EWA, with EWCA demonstrating the strongest predictive effect. However, the direct effects of MGAIUF and MGAIFP on EWA are not statistically significant. Bootstrap mediation analysis further confirms that EWLE and EWCA fully mediate the relationships between multimodal generative AI (MGAI, such as MGAIUF and MGAIFP) and EWA, with writing confidence and attitude serving as the most influential mediating factors. The findings suggest that the effectiveness of MGAI in improving EWA is achieved primarily through promoting students’ learning engagement and strengthening their writing confidence and positive attitudes rather than through direct technological effects. This study enriches the literature on AI-assisted language learning by proposing a “technology–learning behavior–learning outcomes” framework and provides practical implications for the pedagogically effective integration of MGAI into English writing instruction at higher education.
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