Determinants of sustained engagement with generative AI tools in language learning

Abstract

Purpose This study investigates the factors influencing the continuous use of generative artificial intelligence (AI) tools for English language learning among non-native speakers. It aims to address the gap in the literature concerning sustained use in higher education by expanding the “technology continuance theory” (TCT) to include context-specific variables such as information accuracy, perceived enjoyment, and compatibility. Design/methodology/approach Data were collected from 491 non-native English-speaking students in Taiwan via an online survey and were analyzed using “partial least squares structural equation modeling”. Findings The findings indicate that confirmation positively influences satisfaction, perceived enjoyment and perceived usefulness. While perceived enjoyment and perceived usefulness significantly impact satisfaction and sustained use, perceived enjoyment does not significantly affect attitude. Additionally, the moderating roles of information accuracy and compatibility were partially supported. Originality/value This research extends the TCT framework by integrating emotional, functional and contextual variables and offers a holistic understanding of technology continuance in the context of generative AI for language learning.

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