Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning

With the increasing use of large-scale language model-based AI tools in modern learning environments, it is important to understand students’ motivations, experiences, and contextual influences. These tools offer new support dimensions for learners, enhancing academic achievement and providing valuable resources, but their use also raises ethical and social issues. In this context, this study aims to systematically identify factors influencing the usage intentions of text-based GenAI tools among undergraduates. By modifying the core variables of the Unified Theory of Acceptance and Use of Technology (UTAUT) with AI literacy, a survey was designed to measure GenAI users’ intentions to collect participants’ opinions. The survey, conducted among business students at a university in South Korea, gathered 239 responses during March and April 2024. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software (Ver. 4.0.9.6). The findings reveal that performance expectancy significantly affects the intention to use GenAI, while effort expectancy does not. In addition, AI literacy and social influence significantly influence performance, effort expectancy, and the intention to use GenAI. This study provides insights into determinants affecting GenAI usage intentions, aiding the development of effective educational strategies and policies to support ethical and beneficial AI use in academic settings.

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Examining the moderating effect of motivation on technology acceptance of generative AI for English as a foreign language learning

Cited by 3 publications

References 82 publications

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AI Literacy and Intention to Use Text-Based GenAI for Learning: The Case of Business Students in Korea

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