Is Generative AI Reshaping How We Think? Implications for Higher-Order Executive Functions
Authors: Yerin Seung; info@ciddl.org
In today’s education, generative AI tools (e.g., ChatGPT, Gemini, Copilot) are becoming as common as calculators once were. But these new tools do more than solve equations—they can generate essays, suggest solutions, summarize complex information, and help students brainstorm. While these capabilities make learning more accessible and efficient, researchers like León-Domínguez (2024) raise an important question: What happens to our brains when we rely on AI to think for us, especially our higher-order executive functions?
Technology Tools Shape the Brain
Human cognition has always been shaped by cultural tools—from pencils to smartphones. Neuroscience shows that the brain physically adapts to the tools we use, repurposing neural circuits to accommodate reading, calculation, or even tool use. This “neuronal recycling” allows new functions and skills to emerge, but it also means our brains evolve in response to the environments we create. Traditionally, humans have been strategically adopting cognitive offloading strategies to reduce working memory load and expand the tasks they can do. This includes writing a to-do list or using reminders to remember daily tasks, using GPS to navigate, and using a calculator for math equations. But generative AI introduces a new kind of offloading. Students can now bypass core executive processes like understanding a problem, making a plan, and reasoning through steps. León-Domínguez (2024) warns that this kind of high-order offloading could reduce students’ engagement in analytical thinking and problem-solving, which are essential for lifelong learning. When learners regularly offload complex thinking (e.g., planning, reasoning) to generative AI, the brain may shift its resources away from those skills, potentially reshaping how they develop over time.
Three Hypotheses: How Might Generative AI Affect Thinking Over Time?
To better understand the potential long-term consequences of this shift, León-Domínguez (2024) proposed three future scenarios that may emerge from the widespread and habitual offloading of higher-order thinking to generative AI. These scenarios, grounded in cognitive science and neuroplasticity, offer different hypotheses about how human cognition might adapt or atrophy under persistent AI reliance.
- Cognitive Zero-Sum Adjustment: On average, the population’s cognitive skills might not change significantly because the positive effects of AI (e.g., increased productivity) may cancel out the negative effects (e.g., reduced skill use). But benefits might be uneven—those with fewer skills may benefit more, while others stagnate.
- Cognitive Soft Adjustment: Some cognitive functions may be underused but not lost. For example, if students regularly use AI for the first steps of problem-solving (understanding and planning), they might become highly skilled in execution and monitoring but less capable of creating solutions from scratch.
- Cognitive Hard Adjustment: In a dystopian scenario, AI could replace inner speech and introspective thought entirely. Students might become passive actors who follow AI-generated instructions without engaging in reflective thinking, a core aspect of self-regulation and executive control.
What This Means for Educators
These hypotheses are not deterministic outcomes, but cautionary frameworks that provide implications and guidance for education. As educators, we still hold the key to shaping how students engage with AI. Teaching and learning remain essential, not just to acquire knowledge but to foster critical thinking, metacognition, and ethical awareness. Our role as teachers is to help students learn when and how to offload cognitively. Offloading is not inherently bad—it can free up mental energy for deeper thinking. But students must also practice doing the hard work of thinking: framing problems, generating ideas, evaluating alternatives, and reflecting on outcomes. Teacher educators, in particular, can model thoughtful integration of AI: use it as a thought partner, not a substitute for effort. By embedding lessons that require discretion and analysis, we can ensure that AI augments rather than undermines the skills and minds of the next generation.
Final Thought
Generative AI is here to stay, and it will only develop as time goes by. How we use it and how we teach students to use it will determine whether it becomes a powerful scaffold or a cognitive crutch. We need to make sure to guide learners not only to harness AI’s capabilities but also to protect and strengthen their own.
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