Cognitive offloading and the reshaping of human thought: The subtle influence of Artificial Intelligence
DOI:
https://doi.org/10.31207/colloquia.v12i1.185Keywords:
Artificial intelligence, cognitive offloading, memory, critical thinking, creativity, automation bias, education, ethicsAbstract
The integration of artificial intelligence (AI) into daily cognitive tasks has transformed traditional cognitive offloading—the delegation of mental processes to external tools—into a dynamic partnership with intelligent systems. This article examines the dual role of AI-driven cognitive offloading, exploring its potential to enhance efficiency and creativity while posing risks to memory consolidation, critical thinking, and intellectual autonomy. Grounded in extended mind theory (Clark & Chalmers, 1998) and empirical studies like the Google Effect (Sparrow et al., 2011), the analysis reveals how AI's generative capabilities (e.g., ChatGPT, Midjourney) shift offloading from passive storage to delegated thinking, where users adopt AI outputs with minimal scrutiny, fostering automation bias (Logg et al., 2019). In educational contexts, AI tools risk undermining deep learning by reducing retrieval practice and encouraging superficial engagement, as illustrated by a case study of Hatt University, where AI-assisted essays distorted grading systems and eroded student critical analysis. Similarly, in healthcare and finance, overreliance on AI recommendations may compromise professional judgment. To mitigate these risks, the article proposes strategies such as metacognitive training, explainable AI design (Sundar, 2020), and curriculum reforms prioritizing active engagement with AI outputs. Ethical and policy interventions are urged to address epistemic opacity, intellectual property, and the cultural redefinition of authorship. The study underscores the need for balanced AI integration—one that harnesses its benefits while safeguarding human cognitive autonomy.
References
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Carr, N. (2011). The shallows: What the Internet is doing to our brains. W. W. Norton & Company.
Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
Coeckelbergh, M. (2022). AI ethics. The MIT Press. https://doi.org/10.7551/mitpress/12549.001.0001
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Changes in grey matter induced by training. Nature, 427(6972), 311–312. https://doi.org/10.1038/427311a
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. https://doi.org/10.1136/amiajnl-2011-000089
Humphreys, P. (2009). The philosophical novelty of computer simulation. Synthese, 169(3), 615–626. https://doi.org/10.1007/s11229-008-9435-2
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966–968. https://doi.org/10.1126/science.1152408
Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005
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
Selwyn, N., Nemorin, S., Bulfin, S., & Johnson, N. F. (2018). Everyday schooling in the digital age: High school, high tech? Routledge.
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
Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74–88. https://doi.org/10.1093/jcmc/zmz026
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors publishing in our Journal comply to the following terms:
1. Authors keep their work's copyrights, but they guarantee Colloquia to be the first publisher of their papers. They grant the Journal with a Creative Commons Attribution License, under which their work can be shared with the condition that it is appropriately cited.
2. Authors can establish further clauses for non-exclusive distribution, such as publication on a separate book or placing in an institutional data-base. Nevertheless, a note should be always added to explain that the paper was originally published in Colloquia.
This Journal utilizes the LOCKSS system to create a file distributed among participating libraries, allowing these libraries to create permanent archives of the Journal for purposes of preservation and restoration. More information...
