This entry presents a conceptual approach for how artificial intelligence (AI) can be used to support high school and college students’ reading comprehension of complex texts across disciplines, using the Revised Metacognitive Awareness of Reading Strategies Inventory (MARSI-R), as an organizing framework. Drawing on research in literacy, learning sciences, and educational technology, the entry conceptualizes AI tools as potential metacognitive supports that can assist learners in planning, monitoring, and evaluating reading. At the same time, it distinguishes between AI use that risks promoting cognitive outsourcing, particularly when tools replace rather than support readers’ active regulation of meaning-making. The entry emphasizes the importance of instructional design and teacher mediation in aligning AI-supported reading practices with established models of metacognitive strategy use. Central to this discourse is the distinction between cognitive scaffolding, using AI to support and extend students’ strategic engagement within their zone of proximal development, and cognitive outsourcing, using AI to bypass cognitive effort entirely, thereby undermining active meaning-making. A distinctive feature of this entry is its use of MARSI-R not only as an assessment instrument but also as a design heuristic for structuring AI-supported reading interactions. By mapping AI affordances onto MARSI-R’s three strategy dimensions, the entry provides a conceptual bridge between established metacognitive theory and the practical design of AI-enhanced reading environments. This framing distinguishes the present contribution from prior work that treats AI tools and metacognitive frameworks as separate domains. Using MARSI-R’s dimensions of Global, Problem-Solving, and Support reading strategies, this entry describes how AI may provide personalized prompts and feedback that encourage strategic engagement with texts in STEM, the humanities, and social sciences. Illustrative classroom examples and research findings are used to highlight AI’s potential to support students in becoming “architects of their own understanding,” while also addressing ethical considerations such as overreliance on automated summaries and data privacy concerns. This entry offers a practical and theoretically grounded roadmap for integrating AI to support thoughtful, reflective reading across disciplines.
Mokhtari et al. (Sat,) studied this question.