Will, Skill, Not Tool: Chinese university students' acceptance of generative <scp>AI</scp> for academic writing in informal English medium instruction settings

Abstract The generative AI (GenAI) has exhibited diverse functions in supporting academic writing, especially for university students who encounter both disciplinary content and academic English challenges in English medium instruction (EMI). However, existing studies have rarely addressed EMI students' behavioural intentions to use GenAI for academic writing beyond formal classroom settings. By adopting the Will, Skill, Tool (WST) model, this study explores how EMI students' intentions are shaped by emerging AI‐specific variables, including perceived risks, perceived importance of policy (PIP) and AI literacy. Survey data were collected from 512 university students at an EMI university in China. The structural equation model explained 56% of the variance in EMI students' behavioural intentions. The findings indicated that will‐related factors (eg, attitudes, perceived risks, PIP) and the skill factor (AI literacy) were significant antecedents of students' intentions, whereas the tool component (facilitating conditions) was not. Attitudes and perceived risks served as significant indirect pathways through which PIP and AI literacy were associated with behavioural intentions. The findings have implications for pedagogical practices and policy design in promoting EMI students' adoption of GenAI for academic writing. Practitioner notes What is already known about this topic Some Chinese university students use generative AI (GenAI) tools to address both disciplinary and linguistic challenges in academic writing outside English medium instruction (EMI) classrooms. Students' perceived risks, perceived importance of policy and AI literacy have emerged as key predictors of their adoption of GenAI, but the interplay among these variables remains underexplored. Despite the wide application in teachers' technology adoption, the Will, Skill, Tool (WST) model has rarely been adopted to explain students' intention to use GenAI in informal learning contexts. What this paper adds The WST‐based model explained 56% of the variance of Chinese EMI students' behavioural intention to use GenAI for academic writing in informal settings. The will and skill components significantly predicted students' behavioural intentions, whereas the tool element did not. Both attitudes and perceived risks constituted significant indirect mechanisms linking students' perceived importance of policy and AI literacy to behavioural intentions. Implications for practice and/or policy In EMI lessons, teachers can present the benefits of GenAI for addressing both academic content and language issues in academic writing, thus building EMI students' positive attitudes towards this technology. Policymakers should develop clear guidelines on the ethical and responsible use of GenAI in academic writing to alleviate students' concerns about academic misconduct and integrity. EMI lecturers should prioritise students' development of AI literacy by demonstrating effective strategies and critical evaluation skills for students' use of GenAI in academic writing.