AI-Supported Reading Comprehension Across Disciplines

AI-Supported Reading Comprehension Across Disciplines: History

Please note this is an old version of this entry, which may differ significantly from the current revision.

Subjects: Education & Educational Research

Contributor: Kouider Mokhtari , Nirmal Ghimire

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.

artificial intelligence (AI)
metacognitive reading strategies
disciplinary literacy
cognitive scaffolding
generative AI
intelligent tutoring systems

As secondary and postsecondary education institutions grapple with the rapid integration of Artificial Intelligence (AI), the focus of literacy instruction is shifting from the mere consumption of information to the metacognitive management of reading processes [1,2,3,4]. In an era when generative AI can summarize, translate, and analyze texts in seconds, the role of the student reader must evolve alongside these technologies. To remain relevant, readers must move from being passive recipients of content to becoming active “architects of their own understanding.”

AI in education encompasses several distinct categories of technology. Adaptive learning platforms adjust content difficulty and pacing based on learner performance and are widely available in commercial reading programs. Intelligent tutoring systems (ITS) use rule-based or model-tracing approaches to provide step-by-step guidance and have been studied extensively in mathematics and science education. More recently, large language model (LLM)-based tools, such as ChatGPT and similar conversational agents, have introduced open-ended dialog capabilities that can simulate aspects of human reasoning. These tool categories differ in maturity, evidence base, and pedagogical affordances; not all claims in the literature apply equally across them. When applied to literacy, however, each type has the potential to function as a metacognitive scaffold rather than merely providing answers. In this entry, metacognitive reading strategies are defined as the intentional “thinking about thinking” that is required to plan, monitor, and evaluate one’s comprehension of complex academic texts such as dense informational articles, multimodal STEM representations, argumentative essays, and primary source documents, across disciplines [5,6].

The central tension of this entry lies in the distinction between cognitive scaffolding and cognitive outsourcing:

Cognitive scaffolding uses AI to support the student within their zone of proximal development, providing the necessary “boost” to engage with disciplinary texts in STEM, the humanities, or the social sciences that might otherwise be inaccessible.
Cognitive outsourcing occurs when the student uses AI to bypass cognitive struggle entirely, for example, by relying on a chatbot to generate a summary without engaging directly with the source material.

This entry posits that AI tools, when deliberately aligned with the Revised Metacognitive Awareness of Reading Strategies Inventory (MARSI-R), can amplify students’ strategic thinking. Rather than replacing the reader, AI functions as a dialogic partner or silent collaborator, making the invisible thinking moves of expert readers visible and actionable. It is important to note that empirical evidence for AI-supported metacognitive reading is still emerging and often context-dependent; therefore, the claims presented here should be understood as informed by current research rather than as definitive conclusions [7]. Using the MARSI-R as an organizing framework, this entry provides an overview of how AI can enhance:

Global reading strategies (GRS): Goal setting, purpose setting, and intentional pre-reading.
Problem-solving strategies (PSS): Real-time comprehension repair and navigating difficult text.
Support strategies (SRS): Reflection, synthesis, and the use of external aids.

By anchoring these AI tools in a well-tested teaching framework, this entry seeks to provide educators with practical ways to build students’ deep, discipline-specific metacognitive reading skills while guarding against ethical risks and the tendency to let AI do the thinking for them. The sections that follow develop this argument in five parts. The entry first reviews metacognition and strategic reading as a foundation, then introduces MARSI-R as an organizing framework. It next examines how AI tools can scaffold Global, Problem-Solving, and Support reading strategies, followed by a discussion of instructional design and teacher mediation. The entry concludes by addressing considerations of equity, ethics, and instructional risk, and by outlining priorities for future research.

This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia6030056

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