AI as a Metacognitive Mirror: How Students Use AI to Monitor and Repair Reading Comprehension Breakdowns

Abstract

The increasing availability of artificial intelligence (AI) tools has transformed how EFL students engage with academic reading, yet little is known about how AI shapes learners’ metacognitive processes during reading. This qualitative study conceptualizes AI as a metacognitive mirror and investigates how EFL students use AI to monitor and repair reading comprehension breakdowns. Data were collected from undergraduate EFL students at a public university through academic reading tasks, screen recordings, think-aloud protocols, AI interaction logs, and stimulated recall interviews. Thematic analysis revealed that students used AI to externalize comprehension monitoring by confirming interpretations and articulating sources of confusion. AI also supported comprehension repair through strategy-specific and iterative regulation, enabling learners to request paraphrases, examples, and simplified explanations in response to perceived difficulties. However, the findings also indicate tensions between productive metacognitive support and uncritical reliance on AI, particularly when learners accepted AI-generated explanations without verification. The study contributes to AI-assisted reading research by shifting attention from learning outcomes to metacognitive processes and learner agency. Pedagogical implications highlight the importance of guiding students toward reflective and responsible AI use to support academic reading comprehension.

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