Here is a study from MIT showing how use of LLM results in poor understanding. A better understanding comes from doing your own research. The best is to understand it first, then have an LLM restate it.
This paper is quite specific in its methodology and scope, and I think your interpretation extends beyond what it actually supports.
The study focuses on students working with subject matter they may be unfamiliar with. In a specific group, participants used the LLM to generate full essays without engaging with the content themselves. In contrast, control groups wrote essays independently, leading to more meaningful learning outcomes. That contrast is expected and doesn’t equate to a blanket failure of LLMs—it’s a reflection of usage context.
Importantly, the paper doesn’t appear to test scenarios involving guided research or subject-matter-informed refinement using LLMs. In fact, the results show promise when users first write their own work and then use AI tools for revision—the brain activity data in those cases is especially interesting.
In short, the core takeaway is this: relying entirely on LLMs to replace the thinking process results in poor understanding. That’s a reasonable and unsurprising conclusion. But this doesn’t invalidate the use of LLMs in research when the user remains intellectually engaged and applies the tool within a rigorous framework.
If your point is to question methodology assisted by AI, that’s a valuable discussion—but this particular paper doesn't actually explore or critique those kinds of methods. If anything, it underscores the importance of intentional, knowledgeable use of these tools.
It’s also important to consider the nature of the assignment topics. These weren’t straightforward factual essays, but rather open-ended prompts requiring nuanced, subjective responses. Here are the actual prompts given to participants:
- Does true loyalty require unconditional support?
- Must our achievements benefit others in order to make us truly happy?
- Is having too many choices a problem?
- Should we always think before we speak?
- Should people who are more fortunate than others have more of a moral obligation to help those who are less fortunate?
- Do works of art have the power to change people's lives?
- Is it more courageous to show vulnerability than it is to show strength?
- Is a perfect society possible or even desirable?
- Can people have too much enthusiasm?
These are not objective, fact-based questions. They invite diverse, valid perspectives and do not have strictly right or wrong answers. This makes the use case quite specific, and the findings may not generalize even across all forms of essay writing—let alone research contexts that involve structured analysis or domain expertise.
The study used an Enobio EEG headset, which is a reasonably capable research-grade device. Still, like most EEG systems, it's sensitive to motion artifacts—especially during tasks like typing or posture changes. While the device can offer meaningful trends in cognitive engagement, any interpretation of neural activity should be tempered by awareness of these limitations.
Overall, I see this paper as narrowly focused and not broadly applicable to research use cases. Its conclusions are understandable given the methodology and constraints. It doesn’t appear to aim at discrediting LLM use in principle—nor does it succeed in doing so.
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