Wenting Liu
As artificial intelligence (AI) becomes increasingly embedded in language education, AI-supported reading applications have attracted growing interest in second language (L2) learning. By offering immediate assistance and individualized feedback, these tools may help learners process texts more effectively. This study examines higher education students' intention to keep using AI-assisted reading tools for L2 reading. It extends the Technology Continuance Theory (TCT) by adding AI literacy and proposes an Extended Technology Continuance Theory for AI-assisted L2 Reading (ETALR). Data from 424 students were analyzed with partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4, fuzzy-set qualitative comparative analysis (fsQCA) in fsQCA 4.1, and deductive thematic analysis of open-ended responses. The SEM findings support the relevance of TCT in AI-assisted L2 reading and show that AI literacy functions as a meaningful external factor that reinforces several major continuance pathways. The qualitative findings further explain how different aspects of AI literacy shape students’ use of these tools. The fsQCA results identify five configurations associated with high continuance intention. Overall, the study refines TCT by positioning AI literacy as a multidimensional competence and offers implications for educators, learners, and developers who aim to support responsible and sustained use of AI tools in academic reading.