Metacognitive strategy use in GenAI-supported academic reading: a qualitative study of postgraduate students in UK...

In recent years, generative artificial intelligence (GenAI) tools such as ChatGPT have been increasingly integrated into academic reading in higher education. Although GenAI can support processing complex academic texts, its effective use requires learners to employ metacognitive strategies to avoid uncritical reliance. However, how second language (L2) learners use such strategies in GenAI-supported academic reading remains underexplored. Situated in UK higher education, this qualitative study examines how 12 postgraduate L2 students employ metacognitive strategies when using ChatGPT for English academic reading. Data from interviews and retrospective reflections were thematically analyzed, while chat logs were used as supplementary descriptive evidence. The findings identify five categories of metacognitive strategies, namely planning, monitoring, evaluating, information management, and debugging. While many strategies align with prior academic reading research, others are specific to the GenAI context, particularly debugging practices such as correcting GenAI errors and developing personalized prompt templates. Differences were also observed across learners with varying language proficiency, especially in verification and prompt refinement behaviors. This study contributes by providing a qualitative account of metacognitive regulation in GenAI-supported academic reading and extending metacognitive strategy frameworks to GenAI-mediated learning environments.