This report investigates a profound new challenge driven by rapidly expanding use of artificial intelligence (AI) in schooling: the risk that students will outsource too much of the cognitive work that is crucial to establishing the knowledge, skill and ‘thinking infrastructure’ that enables both schooling success and lifelong capacity for ongoing learning and understanding.
There is a growing body of evidence that using AI can short-circuit the cognitive effort required for sustainable, deep learning, with potentially long-term consequences. This cognitive offloading from human to AI is especially risky for school students (‘novice’ learners who are building foundational knowledge and skills) when they turn to AI as a tempting substitute, not an amplifier, increase their dependency on the tool and lose access to deeper learning and critical thinking capabilities. It also introduces extra equity risks for disadvantaged students.
The report reviews the cognitive science behind this concerning shift and the growing evidence of ist impact. It also outlines how these harmful effects can be counteracted through specific teaching and learning strategies and effective design of AI education technology, anchored on bolstering the central role of teachers. It includes specific recommendations for policy and teaching and learning strategies.

The rapid adoption of AI (particularly generative AI) presents a novel challenge to K-12 education. It has the capacity to function as an interactive cognitive partner, bringing the concept of cognitive offloading (outsourcing mental work) to the forefront.
This report analyses this phenomenon through the lens of human cognitive architecture (Cognitive Load Theory), framing the central problem as a conflict: the capacity of AI to bypass the cognitive effort required to build the deep, long-term knowledge that underpins expertise and critical thinking.
The report’s central finding is a critical distinction between two forms of offloading:

• Beneficial offloading occurs when AI is used to manage extraneous cognitive load (e.g., checking grammar), freeing a learner’s limited working memory to focus on essential, intrinsic tasks.
• Detrimental offloading (outsourcing) occurs when a learner uses AI to bypass this intrinsic cognitive effort (the desirable difficulties) required to build long-term knowledge schemas. This offloading also seems to extend to vital metacognitive and self-regulated learning capabilities, compounding the negative impact of outsourcing on learning.

Emerging data support the observation that unstructured AI use trends toward detrimental offloading, creating a performance paradox: students’ short-term performance on tasks improves, while their durable, long-term learning is harmed. This trend appears to be driven by the fluency of AI-generated output, which creates an illusion of competence and encourages metacognitive laziness, leading learners to abdicate the generative effort required to build deep knowledge.
The impact of AI is not primarily technologically deterministic; it is pedagogical. While unstructured use risks cognitive atrophy, the report finds that pedagogically structured interventions, such as explicit teaching, Load Reduction Instruction (LRI), and integrated metacognitive prompts, can successfully foster the self-regulated learning, critical thinking and the deep engagement required for learning.