Cognitive Offloading & Over-reliance

The literature reveals substantial and growing concern about cognitive offloading and over-reliance on AI/LLM tools in K-12 education, with evidence spanning both theoretical frameworks and empirical studies. Multiple papers document how AI tools designed to assist learning can inadvertently create dependency, reduce critical thinking, and bypass the 'productive struggle' essential for deep learning. This concern manifests across multiple educational contexts—from automated question generation and tutoring systems to student use of ChatGPT—with consistent findings that students tend to accept AI-generated content without sufficient verification or critical evaluation. The risk is particularly acute because AI systems are designed for convenience and agreement (the 'comfort-growth paradox'), which can feel empowering while actually constraining cognitive development. Evidence shows students struggle to comprehend AI-generated code, over-rely on AI for tasks they should master independently, and exhibit 'automation bias' where they trust AI outputs even when explicitly warned of potential errors.

However, the literature also identifies promising mitigation strategies through careful pedagogical design. The concept of 'Enhanced Cognitive Scaffolding' proposes that AI should provide temporary, adaptive support that progressively fades as learner competence grows, rather than constant assistance. Studies show that structured learning processes—including pre-use education about AI limitations, limited AI access during tasks to encourage peer/teacher interaction, and post-use verification activities—can reduce over-dependence. The 'extraheric AI' framework specifically advocates for AI that poses questions and alternative perspectives rather than providing direct answers, fostering higher-order thinking. Multiple papers emphasize that AI should augment rather than replace human cognition, requiring explicit teacher training, student digital literacy development, and institutional policies that position AI as a complement to traditional pedagogy. The evidence suggests the risk is real and significant, but manageable through intentional design and implementation that prioritizes cognitive engagement over efficiency.