Abstract
Children now have daily use of artificial intelligence (AI) like voice response systems, conversational agents (e.g., chatbots), and Digital Learning Systems that provide an AI assist to support their learning. The use of such technology is expected to offer children personalized chances for educational enhancement and cognitive assistance when used appropriately. However, there is limited research on the effect of technology on the developmental/maturational process of children through cognitive offloading; thus, there is insufficient evidence of one or more elements leading to cognitive development through cognitive offloading by adults (i.e., complete adults) who have already developed their cognitive capacity. This paper integrates the current cognitive psychology, developmental neuroscience, and generative-AI-in-education literature to address the above-stated gap in knowledge. The proposed constructs are hierarchically related: The Developmental Offloading Hypothesis (DOH), a general principle confirming that cognitive offloading results in different kinds of effects when components of competence form as opposed to when they are being applied; The Offloading Window (OW), the contextual condition that defines whether the DOH is developmentally active for any particular child at any point in time based on their individual capacity and pattern of use; and the Scaffolded Cognitive AI Integration Framework (SCAIF), the operational framework for translating the principles of DOH and OW into practical guidelines that differentiate scaffolds from substitutions. Each construct is defined behaviourally, evaluated against alternative theoretical accounts (including the Extended Mind, Distributed Cognition, Vygotskian scaffolding, and Cognitive Load Theory), and expressed as a set of falsifiable propositions. The paper concludes with a roadmap for the empirical work required to test these propositions, including a planned mixed-methods study of parents and early-childhood educators.
References
Alfarwan, A. (2025). Generative AI use in K–12 education: A systematic review. Frontiers in Education, 10, Article 1647573. https://doi.org/10.3389/feduc.2025.1647573
Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4(11), 417–423. https://doi.org/10.1016/S1364-6613(00)01538-2
Bjork, E. L., & Bjork, R. A. (2014). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher and J. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (2nd edition). (pp. 59-68). New York: Worth. https://bjorklab.psych.ucla.edu/publication/bjork-e-l-bjork-r-a-2014-making-things-hard-on-yourself-but-in-a-good-way-creating-desirable-difficulties-to-enhance-learning-in-m-a-gernsbacher-and-j-pomerantz-eds-psycholo/
Bouguettaya, S., Pupo, F., Chen, M., & Fortino, G. (2025). A meta-survey of generative AI in education: Trends, challenges, and research directions. Big Data and Cognitive Computing, 9(9), Article 237. https://doi.org/10.3390/bdcc9090237
Casey, B. J., Tottenham, N., Liston, C., & Durston, S. (2005). Imaging the developing brain: What have we learned about cognitive development? Trends in Cognitive Sciences, 9(3), 104–110. https://doi.org/10.1016/j.tics.2005.01.011
Center on the Developing Child. (2016). From best practices to breakthrough impacts: A science-based approach to building a more promising future for young children and families. Harvard University. https://developingchild.harvard.edu/resources/report/best-practices-breakthrough-impacts/
Clark, A., & Chalmers, D. J. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135–168. https://doi.org/10.1146/annurev-psych-113011-143750
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2019). The online brain: How the Internet may be changing our cognition. World Psychiatry, 18(2), 119–129. https://doi.org/10.1002/wps.20617
Garzón, J., Baldiris, S., & Gutiérrez, J. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), Article 84. https://doi.org/10.3390/mti9080084
Gu, X., & Ericson, B. J. (2025). AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review. In Proceedings of the 2025 ACM Conference on International Computing Education Research V.1 (ICER '25). Association for Computing Machinery, New York, NY, USA, 125–140. https://doi.org/10.1145/3702652.3744217
Hu, X., Xu, S., Tong, R., & Graesser, A. (2025). Generative AI in education: From foundational insights to the Socratic playground for learning. Computers and Education: Artificial Intelligence, 8, Article 100311. https://doi.org/10.48550/arXiv.2501.06682
Jensen, L. X., Buhl, A., Sharma, A., & Bearman, M. (2025). Generative AI and higher education: A review of claims from the first months of ChatGPT. Higher Education, 89, 1145–1161. https://doi.org/10.1007/s10734-024-01265-3
Johnson, M. H. (2001). Functional brain development in humans. Nature Reviews Neuroscience, 2(7), 475–483. https://doi.org/10.1038/35081509
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669
Knudsen, E. I. (2004). Sensitive periods in the development of the brain and behavior. Journal of Cognitive Neuroscience, 16(8), 1412–1425. https://doi.org/10.1162/0898929042304796
Kolb, B., & Gibb, R. (2011). Brain plasticity and behaviour in the developing brain. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20(4), 265–276. https://pmc.ncbi.nlm.nih.gov/articles/PMC3222570/
Mosier, K. L., Skitka, L. J., Dunbar, M., & McDonnell, L. (2001). Aircrews and Automation Bias: The Advantages of Teamwork? The International Journal of Aviation Psychology, 11(1), 1–14. https://doi.org/10.1207/S15327108IJAP1101_1
Nadelson, L. S. (2025). The use of generative artificial intelligence in K–12 education: An introduction to the special issue of the Journal of Educational Research. The Journal of Educational Research, 118(6), 531–534. https://doi.org/10.1080/00220671.2025.2514913
Piaget, J. (1952). The origins of intelligence in children. International Universities Press.
Pinho, I., Costa, A.P. & Pinho, C. (2025). Generative AI governance model in educational research: A scoping review. Frontiers in Education, 10, Article 1594343. https://doi.org/10.3389/feduc.2025.1594343
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Roe, J., & Perkins, M. (2024). Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis. https://doi.org/10.48550/ARXIV.2411.00631
Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778. https://doi.org/10.1126/science.1207745
Solberg, M. & Hutchins, E. (2023): Cognition in the wild: MIT Press, Cambridge, 1995, pp. 402. ISBN: 9780262581462. WMU Journal of Maritime Affairs, 22, 267–272. https://doi.org/10.1007/s13437-023-00305-6
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. 174 pp., Harvard University Press. https://www.hup.harvard.edu/books/9780674576292
Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain drain: The mere presence of one’s own smartphone reduces available cognitive capacity. Journal of the Association for Consumer Research, 2(2), 140–154. https://doi.org/10.1086/691462
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7

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