Imagine this: It’s the 1950s. You’re slouched over your school desk, chewing your pencil, panicking about what to write for your assignment on some author’s biography. The library is closed. Your parents don’t know. There’s no one to ask.

Fast forward to today. The same question can be answered as fast as you can type into ChatGPT or similar LLMs.

When ChatGPT became publicly available, it wasn’t just students or researchers who embraced it. It was everyone: job seekers, overworked parents, writers with deadlines, students, everyone with a question to ask. Large language models (LLMs) quickly ran through their academic novelty and became tools for thought and daily use. But what does it mean when cognition is increasingly mediated by a system that answers faster than we can fully think?

A 2024 study from provides one of the clearest early glimpses into this shift. Conducted among undergraduate students in Ghana, the research found that using ChatGPT significantly improved participants’ critical, creative, and reflective thinking skills, when embedded in a guided educational context. The findings are optimistic: AI, it seems, can genuinely support cognitive growth.

But what happens when we move beyond the classroom? In the real world full of challenges and for this context, full of information influx, what comes out from even more consummation for every thought you can type out? This article examines the wider implications of ChatGPT’s integration into everyday cognitive life. It asks not just what these models can do for us, but what they are doing to us.

ChatGPT and the New Confidence in Learning

The positive findings of the Ghana study are backed up by impressive statistics. Students exposed to ChatGPT in a flipped-classroom model, where AI prompts were used as part of active learning exercises, demonstrated measurable gains in all three domains: critical, creative, and reflective thinking. In other words, ChatGPT saved time and enhanced the quality of thought output.

Students reported feeling more confident, more engaged and more capable of applying academic concepts to real-life scenarios. “I could control and suggest how I want it to explain my prompts,” one student noted. Another described the interaction as “feeling like I was communicating with my lecturer.” In short, they weren’t just using ChatGPT as a calculator, but a tool with actual usage.

Extrapolated to the general population, this finding carries enormous promise. For those excluded from traditional education, whether due to geography, age, disability, or socioeconomic status, LLMs represent a shift in easier access. Adults returning to learning can clarify unfamiliar topics without shame. Immigrants can practice new languages on demand. Teenagers can explore advanced ideas without gatekeepers. Elderly users can engage with evolving cultural and technological discourses, regaining a sense of intellectual agency.

Crucially, ChatGPT accommodates different learning speeds. It waits without judgment. It reformulates answers. It offers analogies. It suggests what to ask next. In a world where traditional education often penalizes neurodivergence or non-standard inquiry paths, this responsiveness is a liberation. However, even liberation comes with terms and conditions. And not all cognitive support is neutral as we have covered in the consequences of sycophancy in LLM models.

When Thinking Feels Optional

While the Ghana study shows promise for guided educational use of ChatGPT, the cognitive landscape looks very different in unguided, real-world contexts. A growing body of research warns that LLMs can encourage cognitive shortcutting. In their 2023 analysis, Yuan et al. found that ChatGPT-generated explanations, while coherent, often substitute perceived authority for actual comprehension, especially among users with low domain knowledge. Similarly, Bang et al. (2023) demonstrated that ChatGPT regularly “hallucinates” – generating factually incorrect or invented responses, while maintaining high linguistic confidence. The Ghana study confirms this risk: 80% of students in the experimental group reported encountering false citations or fabricated content.

When outputs sound plausible, users rarely verify. According to OpenAI’s own evaluations, users trust AI-generated responses over human responses in a significant percentage of cases, particularly when the answers are delivered with syntactic fluency and affective tone.

This aligns with our previous critique of AI “empathy engines” in grief tech and mental health apps: when machines simulate care or intelligence, they bypass effortful introspection.

A 2023 article from Li et al. found that participants using ChatGPT for ethical reasoning tasks scored lower in post-task metacognitive reflection than those using traditional sources. The authors hypothesized that ChatGPT’s “wraparound fluency” discourages users from engaging with the uncertainty necessary for deep ethical inquiry.

This raises some questions: How does overuse of AI tools affect epistemic humility? Are we trading in the discomfort of thinking for the convenience of agreement? When users mistake ChatGPT’s authority for accuracy, the results are just wrong. Worse, as shown by Borji (2023), ChatGPT tends to generate answers that align with user expectations, especially when users seek confirmation. This reinforces preexisting biases under the guise of objectivity.

Personalization Without Pedagogy

One of ChatGPT’s most celebrated features is its ability to personalize responses. It reformulates, elaborates, and adapts based on user feedback. At first glance, this seems like the holy grail of education, tailored instruction and work at scale. But personalization, when taken away from pedagogical intent, risks simulating understanding rather than producing it.

The Ghana study showed that reflective thinking improved most significantly when students were guided by prompts specifically designed to foster metacognition. Prompts like “Which research design fits your question, and why?” elicited justification, comparison, and introspection. But outside this framework, ChatGPT often operates as an intellectual vending machine: insert a question, get a fluent answer. The user is rarely encouraged to pause, interrogate or resist the output.

This distinction mirrors findings by Kardan & Plonsky (2023), who found that students who used ChatGPT for language learning performed well on surface-level tasks but struggled with delayed recall and generalization. Fluency, they argued, created a false sense of mastery—a phenomenon dubbed the “automation illusion.”

Even more concerning is the erosion of metacognitive awareness. In a recent study by Zamfirescu et al. (2024), users who relied on AI assistance in writing tasks reported higher confidence in their reasoning but performed worse on tasks requiring synthesis and original thought. The researchers concluded that AI systems, while boosting perceived efficacy, can actively undermine actual competence when reflective scaffolding is absent.

The educational field has long emphasized the importance of “productive struggle” and “desirable difficulties” (Bjork & Bjork, 2011). Learning is deepened not by convenience but by confrontation, focused learning time with new information and competing ideas. Yet ChatGPT, in its current form, is not designed to introduce friction. It is optimized to please, not to challenge.

In this context, the personalization it offers becomes paradoxical. It gives users what they want, not what they need to grow. Like a mirror that reflects your face but never your blind spots, it affirms rather than expands. And in a world increasingly flooded with AI-generated content, this becomes a critical danger: learners think they are learning, when they are only looping. The risks of misuse are rising; examples being used inappropriately for “slop” and political propaganda.

Conclusion: Increasing Literacy and Global Regulations

ChatGPT is not just a writing tool, nor merely a conversational novelty. It is a cognitive infrastructure, one that now undergirds everything from student assignments to medical queries, corporate decisions and even political discourse.

This is not to say that ChatGPT should not be used, but it shouldn’t be taken for granted. The first step is literacy: users must understand that not all answers are equal, that fluency is not accuracy, and that even the most eloquent response can be wrong, biased or hollow.

But literacy alone is not enough. We need global regulatory standards that ensure transparency, accountability, and oversight of LLMs, particularly in contexts where their outputs can influence health, education, law, or vulnerable populations. These standards must require clear disclosure of AI involvement, traceability of training data and human-in-the-loop safeguards for high-risk interactions.

Platforms should be obligated to implement epistemic friction: features that encourage verification, offer opposing views, or highlight uncertainty. Just as we regulate pharmaceuticals not only for efficacy but for potential misuse, we must treat language models with the same caution. Their ability to feel right makes them especially dangerous when they are wrong.

Finally, we must ask ourselves: are we building tools that help us think or that think for us? We owe it to future generations to make sure that instant answers don’t come at the cost of lifelong thinking.