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 its 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.
Artificial intelligence, cognitive offloading and implications for education
This report investigates the challenge driven by the 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 knowledge, skill and ‘thinking infrastructure’. The report includes specific recommendations for policy and teaching and learning strategies.
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
The proliferation of artificial intelligence (AI) tools has transformed numerous aspects of daily life, yet its impact on critical thinking remains underexplored. This study investigates the relationship between AI tool usage and critical thinking skills, focusing on cognitive offloading as a mediating factor. Utilising a mixed-method approach, we conducted surveys and in-depth interviews with 666 participants across diverse age groups and educational backgrounds. Quantitative data were analysed using ANOVA and correlation analysis, while qualitative insights were obtained through thematic analysis of interview transcripts. The findings revealed a significant negative correlation between frequent AI tool usage and critical thinking abilities, mediated by increased cognitive offloading. Younger participants exhibited higher dependence on AI tools and lower critical thinking scores compared to older participants. Furthermore, higher educational attainment was associated with better critical thinking skills, regardless of AI usage. These results highlight the potential cognitive costs of AI tool reliance, emphasising the need for educational strategies that promote critical engagement with AI technologies. This study contributes to the growing discourse on AI’s cognitive implications, offering practical recommendations for mitigating its adverse effects on critical thinking. The findings underscore the importance of fostering critical thinking in an AI-driven world, making this research essential reading for educators, policymakers, and technologists.
The Personalized Learning Revolution
The allure of AI lies predominantly in its unmatched potential for efficiency, convenience, and accuracy. However, this unprecedented convenience brings with it a hidden yet profound threat: the subtle erosion of human capacity for critical thinking through cognitive offloading.
The risks of AI in schools outweigh the benefits, report says
A new report warns that AI poses a serious threat to children's cognitive development and emotional well-being.
Does AI stop children from learning?
New data show the peril and promise of the technology
Pupils in England are losing their thinking skills because of AI, survey suggests
Two-thirds of secondary school teachers report a decline in core abilities such as writing and problem-solving
ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention
The rapid integration of generative artificial intelligence into higher education has outpaced empirical understanding of its effects on fundamental learning processes. To address this gap, this randomized controlled trial (n = 120) tested ChatGPT's impact on long-term knowledge retention in undergraduates learning AI. Participants were randomly assigned either to use ChatGPT as a study aid (AI-Assisted Group) or to use only traditional, non-AI study methods (traditional learning group). Knowledge retention was assessed with a surprise test 45 days after learning. Students who used ChatGPT scored significantly lower on the retention test (57.5 % correct) compared to those who studied traditionally (68.5 % correct), t (83) = −3.19, p = .002, Cohen's d = 0.68. This suggests that unrestricted ChatGPT use impaired long-term retention, likely by reducing the cognitive effort that supports durable memory. The findings align with cognitive offloading theory and the ‘desirable difficulties’ principle: while AI assistance may ease initial learning, it appears to undermine the effortful processes needed for robust learning. These results have important implications for how generative AI tools should be integrated into higher education.
‘I hate what AI is doing to the minds and happiness of the young’: Katherine Rundell on the view from the classroom
Education is at a crossroads, argues the author and academic. Should we embrace new technology in the name of efficiency, or is it time to fight back?
A new direction for students in an AI world: Prosper, prepare, protect | Brookings
This report explores the potential risks generative AI poses to students and outlines what we can do now to minimize them.
Adults Lose Skills to AI. Children Never Build Them.
Discussions of cognitive offloading often miss a critical distinction: What AI does to a 45-year-old's brain is categorically different from what it does to a 14-year-old's.
What counts as evidence in AI & ED: Towards Science-for-Policy 3.0
Abstract Since the 1990s, there have been heated debates about how evidence should be used to guide teaching practice and education policy, and how educational research can generate robust and trustworthy evidence. This paper reviews existing debates on evidence-based education and research on the impacts of AI in education and suggests a new conceptualisation of evidence aligned with an emerging learning-oriented model of science-for-policy, which we call S4P 3.0. Existing empirical evidence on AIED suggests some positive effects, but a closer look reveals methodological and conceptual problems and leads to the conclusion that existing evidence should not be used to guide policy or practice. AI is a new type of technology that interacts with human cognition, communication, and social knowledge infrastructures, and it requires rethinking what we mean by “learning outcomes” and policy and practice-relevant evidence. A common belief that AI-supported personalisation will “revolutionise” education is historically rooted in a methodological confusion that we call the Bloomian paradox in AIED, and based on a limited view on the social functions of education.
What counts as evidence in AI & ED: Towards Science-for-Policy 3.0
Abstract Since the 1990s, there have been heated debates about how evidence should be used to guide teaching practice and education policy, and how educational research can generate robust and trustworthy evidence. This paper reviews existing debates on evidence-based education and research on the impacts of AI in education and suggests a new conceptualisation of evidence aligned with an emerging learning-oriented model of science-for-policy, which we call S4P 3.0. Existing empirical evidence on AIED suggests some positive effects, but a closer look reveals methodological and conceptual problems and leads to the conclusion that existing evidence should not be used to guide policy or practice. AI is a new type of technology that interacts with human cognition, communication, and social knowledge infrastructures, and it requires rethinking what we mean by “learning outcomes” and policy and practice-relevant evidence. A common belief that AI-supported personalisation will “revolutionise” education is historically rooted in a methodological confusion that we call the Bloomian paradox in AIED, and based on a limited view on the social functions of education.
Mayor Mamdani and Chancellor Samuels Put Students First with Nation’s Broadest Generative AI Moratorium in Schools
NEW YORK – Mayor Zohran Kwame Mamdani and Schools Chancellor Kamar H. Samuels today announced a moratorium on student-facing generative Artificial Intelligence (AI) use in schools, alongside a new screen time policy, a decision that will impact nearly 600,000 public school students, or two-thirds of the system’s total enrollment. The policy establishes a one-year moratorium, effective in the 2026-2027 school year, on student-facing generative AI for children in 2-K through 8th grade. It also introduces twice-yearly AI critical thinking modules for high schoolers, limited AI pilots for a small number of high school classrooms and age-appropriate screen time restrictions.
Are we offloading too much of our thinking to AI?
Reflections on autonomy and the value of thinking for ourselves
Eleven Things I Wish Educators Understood About AI and What it Means for "Education"
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