Exploring the adoption of AI-enabled English learning applications among university students using extended UTAUT2 model

The rapid growth of technology has penetrated the realm of education, reshaping old teaching philosophies and improving educational approaches. Notably, the influence of Artificial Intelligence (AI) on English learning has become momentous. However, a critical issue is the lack of autonomy in AI-enabled English learning applications, leading to lower engagement of students. The study investigates students’ adoption intention and usage behavior to embrace AI-enabled English learning applications in China. Leveraging the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, extended with Perceived Enjoyment and Personal Innovativeness, this study provides a nuanced understanding of student adoption through delving into the applications’ attractiveness and users’ inherent tendencies toward technology adoption. Data from 1167 undergraduate students at Shandong University were collected using an online survey with simple random sampling and analyzed through the partial least square-structure equation modeling (PLS-SEM) technique. The results highlight the positive influence of performance expectancy (β = 0.294), perceived enjoyment (β = 0.186), personal innovativeness (β = 0.341), and price value (β = 0.159) on adopting AI-enabled English learning applications, accentuating that students with a greater propensity for exploring new technologies are more likely to adopt these applications. However, mediating effect analysis indicates that only performance expectancy (β = 0.075), personal innovativeness (β = 0.087), and perceived enjoyment (β = 0.048) significantly influence usage behavior through adoption intention, suggesting the primacy of innovative features and engaging content in the applications. This study provides a theoretical expansion of UTAUT2, leading to a refined and education focused version of UTAUT2. Future research is suggested to involve gender and frequency of app usage into the study, and conduct longitudinal studies to scrutinize the long-term impact and sustainability of AI-enabled learning applications.

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