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The Impact of Generative Artificial Intelligence Use on Perceived English Learning Achievement: The Roles of Use
1Werklund School of Education, University of Calgary, Calgary, AB T2N 1N4, Canada.
Behavioral Sciences (Basel, Switzerland)
|May 27, 2026
Summary
Generative artificial intelligence (GAI) use in higher education English learning is predicted by user expectations and support. GAI use influences learning achievement, especially when its features fit task needs, enhancing educational outcomes.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Language Learning Technologies
Background:
- Generative artificial intelligence (GAI) offers potential for English language learning in higher education.
- The relationship between student perceptions, GAI use, and learning achievement requires further investigation.
- Existing technology acceptance models need adaptation for AI-driven educational contexts.
Purpose of the Study:
- To investigate the factors influencing undergraduate students' adoption and use of GAI for English learning.
- To examine the mediating role of GAI use behavior on the relationship between acceptance factors and perceived learning achievement.
- To explore the moderating effect of Task-Technology Fit (TTF) on the link between GAI use and learning outcomes.
Main Methods:
- A quantitative study employing covariance-based structural equation modeling (CB-SEM).
- Data collected from 537 undergraduate students across five public universities in China.
- Utilized constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) and TTF theory.
Main Results:
- Performance expectancy, effort expectancy, facilitating conditions, perceived competitiveness, and AI self-efficacy significantly predicted GAI use.
- GAI use behavior mediated the relationship between these predictors and perceived English learning achievement.
- Task-Technology Fit moderated the association between GAI use behavior and learning achievement, with stronger effects when GAI aligned with task requirements.
Conclusions:
- Student perceptions and enabling conditions are key drivers of GAI adoption in English language learning.
- GAI use behavior is a crucial mediator linking acceptance factors to learning achievement.
- The alignment of GAI functionalities with specific learning tasks amplifies its positive impact on perceived achievement, underscoring the importance of TTF.
