ArticleFrontiers in psychology2026
Determinants of university students' intention to use generative AI tools for personalized English learning: mediating effect of flow experience and moderating effect of personal innovativeness.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Generative AI technology offers an efficient, engaging, and personalized learning experience for language learning. To encourage the utilization of generative AI tools in language study, this study has expanded the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to pinpoint determinants shaping university students' willingness to take these tools for personalized English learning, evaluate the mediating influence of flow experience, and analyze the moderating role of personal innovativeness. Methods: A survey was conducted among university students using a convenience sampling method, from which 386 valid questionnaires were collected for subsequent analysis. Structural equation modeling was employed to analyze the collected data with the aid of SmartPLS 4.0 software. Results: The findings indicated that performance expectancy, effort expectancy, hedonic motivation, and habit significantly affect students' behavioral intentions. Flow experience takes a partially complementary mediating role in the connections between performance expectancy, hedonic motivation, habit, and behavioral intention. Notably, the research also uncovered that personal innovativeness acts as a moderator in the link between hedonic motivation and behavioral intention. This means that for students with a higher level of personal innovativeness, the positive association between hedonic motivation and behavioral intention is stronger. Discussion: The research results provide practical implications for multiple stakeholders: for students, they can make more informed decisions to choose suitable technological tools to maximize language learning performance; for language instructors, they can better integrate new technological tools into teaching practices; for GenAI tool designers, they can develop more effective, engaging, and user-friendly products that align with university students' needs and preferences for language learning. The extended UTAUT2 model also enriches the theoretical understanding of technology acceptance in the context of personalized language learning with GenAI.
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