Adopting GenAI-Assisted Writing Feedback in L2 Learning: A UTAUT Study of Medical Students
Keywords:
English Writing Feedback; Generative Artificial Intelligence (GenAI); Medical Students; Technology Acceptance; UTAUTAbstract
The use of generative artificial intelligence (GenAI) in English as a Foreign Language (EFL) writing instruction has created new opportunities to support students’ English language writing. Yet little is known about the factors that influence medical students’ acceptance of GenAI-assisted writing feedback. This study modified the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of medical students’ adoption of GenAI-assisted English language writing feedback. A cross-sectional survey was conducted among 357 medical students at a Chinese medical university. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The proposed model showed satisfactory explanatory power (R² = 0.475 for use behaviour) and predictive relevance (Q² > 0.41). All hypothesised relationships were significant. Social influence was the strongest predictor of use behaviour (? = 0.274, p < .001), followed by performance expectancy. Intention to use significantly mediated the relationships between the antecedent variables and use behaviour, indicating partial mediation. Social influence and performance expectancy also had stronger total effects than effort expectancy and facilitating conditions. The findings extend the application of the UTAUT model to medical students’ English language education. They suggest that successful implementation of GenAI-assisted writing feedback depends on both clear educational value and strong support from teachers and peers. These factors may encourage continued student use of the technology.
https://doi.org/10.26803/ijlter.25.8.10
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