If we offload thinking to AI, if we habitually shift thinking, remembering and problem-solving to machines, do we lose mental skill? 16/11/25

As AI assistants become fluent collaborators — drafting emails, summarizing research, generating code and even composing essays — researchers are asking a blunt question: if we habitually shift thinking, remembering and problem-solving to machines, do we lose mental skill? Recent empirical work, long-standing cognitive science, and emerging reviews point to a credible risk: habitual cognitive offloading to AI can produce measurable declines in the neural and behavioral markers of effortful thinking, memory, and critical reasoning. Below I summarize the evidence and explain the likely mechanisms, note limitations, and  practical responses to this dillema, machine v/s human. 

What the evidence says

Neural and behavioral signals of reduced engagement.
A recent experimental study led by MIT researchers (Kosmyna et al, 2025) measured brain activity (EEG connectivity) while participants wrote essays in three conditions: using a large language model (LLM), using a search engine, or relying only on their own brains. LLM users showed weaker, less distributed brain-connectivity patterns associated with executive control, memory and creative thought, and—critically—those reductions persisted when some participants later tried to write without the LLM. The authors describe this pattern as an accumulation of cognitive debt from repeated offloading to AI.  

Longer history: the “Google effect” and external memory.
The idea that tools change what we remember is not new. Sparrow et al. (2011) showed that when people expect to be able to look up information later, they remember the information itself less well and instead remember where to find it (a form of external or transactive memory). That pattern—digital or external memory replacing internal memory—is a foundational finding that helps explain why AI might erode recall and practice. 

Consistent evidence linking AI/tool use with weaker critical thinking.
Mixed-method and large-sample studies and reviews from 2024–2025 report negative correlations between heavy AI/tool use and measures (Gerlich,2025) of critical thinking, cognitive effort, creativity and academic skills. Several meta-analyses and field studies find that increased reliance on algorithmic answers is associated with lower performance on tasks requiring independent reasoning and original composition. These findings appear across education settings and adult samples.  

Cognitive-effort and learning outcomes.
Experimental work examining cognitive effort finds that when AI produces finished or near-finished outputs (e.g., full paragraph drafts or worked solutions), learners expend less effort, learn less deeply and show lower retention than when they must generate or struggle with a problem themselves. A 2025 open-access review of generative-AI effects reports systematic reductions in measured cognitive effort under some usage patterns. 

How offloading to AI can weaken cognition — plausible mechanisms

  1. Practice-use tradeoff. Cognitive skills (writing, reasoning, memory retrieval) improve with repeated practice. If AI performs those acts for us repeatedly, there are fewer opportunities for the neural circuits that support those skills to strengthen (muscles unused atrophy.)  

  2. Externalization of memory (transactive memory). Knowing that information is externally stored and easily retrievable (search engines, AIs) reduces the incentive to encode facts deeply into long-term memory(Sparrow et al, 2011) . Over time, this reorganizes cognitive strategy toward “where to look” rather than “what I know.”  )

  3. Reduced cognitive effort and metacognitive monitoring. When an AI (Chen et al.,2025) provides an answer, users are less likely to generate hypotheses, evaluate alternatives, or monitor their own understanding — processes essential for critical thinking. Reduced metacognitive practice weakens self-evaluation and learning strategies. 

  4. Automation bias and overtrust. People (Gerlich,2025) can come to overtrust AI outputs and accept them without verification. Over time, this externalizes judgment and degrades analytical vigilance.  

  5. Neural under-engagement. The MIT EEG (Kosmyna et al, 2025) study suggests that reduced neural connectivity in executive and memory networks can occur with habitual AI use—an effect that may persist beyond the immediate interaction, at least in the short term. 

How strong is the evidence? (Limitations and open questions)

  • Sample sizes and generalizability. Some of the most direct neural evidence (EEG study) used modest samples and is early (preprint/public release). Larger, longer longitudinal studies (Kosmyna et al, 2025) are needed to confirm persistence and population-level impact. 

  • Causation vs. selection. Surveys showing correlations between AI (Gerlich,2025) use and lower critical thinking may conflate cause and effect: people with weaker skills could be more likely to use AI heavily. Experimental work helps, but more randomized longitudinal studies are required. 

  • Task dependence. Not every use of AI is equal. Using AI (Chen et al.,2025) as a tutor (guided hints, Socratic prompts) may improve learning, while using AI as a shortcut to finished products is more likely to degrade skill. Context and interface design matter hugely.  

  • Individual differences. Age, baseline skill, motivation, and domain moderate effects: novices may benefit more from scaffolded AI help, while habitual reliance among learners can harm developing skills (Jinrui & Zhang, 2025).

Individuals, educators and professionals should:

For individuals

  • Use AI (Chen et al.,2025) as a partner, not a replacement. Ask it to generate multiple outlines, critiques, or counterarguments rather than final drafts. Then do the composing, editing and citing yourself.  

  • Practice retrieval. Purposefully force yourself to recall facts before consulting AI (self-testing). This counters (Sparrow et al, 2011) the “Google effect.”  

  • Reflect and journal: after using an AI (Gerlich,2025) , write in your own words what you learned and why you accepted or rejected its suggestions (metacognitive practice).

For educators and institutions

  • Design assignments (Chen et al.,2025) that require process evidence (drafts, annotated reasoning, show work) so students can’t outsource the thinking entirely.

  • Teach how to use AI effectively: critical prompts, cross-checking, evaluating sources, and tracing chain-of-thought rather than copy-pasting model outputs.

  • Use AI (Chen et al.,2025) as a scaffold for learning (hint systems, error detection) rather than as a finishing tool that yields complete answers. 

For designers and policymakers

  • Build “cognitive forcing functions” into AI interfaces:(Chen et al.,2025)  require users to attempt an answer, rate confidence, or explain reasoning before showing a full solution. Evidence suggests such interventions reduce overreliance. 

  • Provide transparent indicators of uncertainty and provenance so users are prompted to verify rather than blindly accept outputs.(Gerlich,2025)  

  • Fund longitudinal research (Kosmyna et al, 2025) to track learning, professional competence, and neural markers over time to understand long-term effects. The current experimental work calls for larger and longer follow-ups.

Conclusion

There is growing, convergent evidence that habitual offloading of thinking and remembering to AI can reduce cognitive effort, alter memory strategies, and — in some experimental settings — produce measurable reductions in brain activity linked to executive control and memory. The phenomenon is not inevitable: careful pedagogy, mindful individual habits, and thoughtful interface design can preserve and even enhance human cognition while still reaping AI’s benefits. But the research is a clear warning: adopt AI tools deliberately, teach users how to use them critically, and design systems that keep people doing the thinking that matters.

References  

  • Jinrui Tian, Ronghua Zhang, 2025. Learners' AI dependence and critical thinking: The psychological mechanism of fatigue and the social buffering role of AI literacy,Acta Psychologica,Volume 260,2025,105725,ISSN 0001-6918, https://doi.org/10.1016/j.actpsy.2025.105725.

  • Kosmyna, N., Hauptmann, E., Yuan, Y. T., et al. Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task (preprint; MIT Media Lab). 2025.  

  • Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips. (Foundational study on externalized memory).  

  • Gerlich, M. (2025). AI Tools in Society: Impacts on Cognitive Offloading and Critical Thinking (MDPI—mixed methods review).  

  • Chen, Y., et al. (2025). Effects of generative artificial intelligence on cognitive effort (open access review/experiment).

  • Meta-analytic and review articles on the Google effect / digital amnesia and on AI’s educational effects