Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
Project link: https://www.brainonllm.com/
Figures: https://www.brainonllm.com/figures
Paper on arxiv (pdf): https://arxiv.org/pdf/2506.08872
With today’s wide adoption of LLM products like ChatGPT from OpenAI, humans and businesses engage and use LLMs on a daily basis. Like any other tool, it carries its own set of advantages and limitations. This study focuses on finding out the cognitive cost of using an LLM in the educational context of writing an essay.
We assigned participants to three groups: LLM group, Search Engine group, and Brain-only group, where each participant used a designated tool (or no tool in the latter) to write an essay. We conducted 3 sessions with the same group assignment for each participant. In the 4th session we asked LLM group participants to use no tools (we refer to them as LLM-to-Brain), and the Brain-only group participants were asked to use LLM (Brain-to-LLM). We recruited a total of 54 participants for Sessions 1, 2, 3, and 18 participants among them completed session 4.
We used electroencephalography (EEG) to record participants’ brain activity in order to assess their cognitive engagement and cognitive load, and to gain a deeper understanding of neural activations during the essay writing task. We performed NLP analysis, and we interviewed each participant after each session. We performed scoring with the help from the human teachers and an AI judge (a specially built AI agent).
We discovered a consistent homogeneity across the Named Entities Recognition (NERs), n-grams, ontology of topics within each group. EEG analysis presented robust evidence that LLM, Search Engine and Brain-only groups had significantly different neural connectivity patterns, reflecting divergent cognitive strategies. Brain connectivity systematically scaled down with the amount of external support: the Brain‑only group exhibited the strongest, widest‑ranging networks, Search Engine group showed intermediate engagement, and LLM assistance elicited the weakest overall coupling. In session 4, LLM-to-Brain participants showed weaker neural connectivity and under-engagement of alpha and beta networks; and the Brain-to-LLM participants demonstrated higher memory recall, and re‑engagement of widespread occipito-parietal and prefrontal nodes, likely supporting the visual processing, similar to the one frequently perceived in the Search Engine group. The reported ownership of LLM group’s essays in the interviews was low. The Search Engine group had strong ownership, but lesser than the Brain-only group. The LLM group also fell behind in their ability to quote from the essays they wrote just minutes prior.
As the educational impact of LLM use only begins to settle with the general population, in this study we demonstrate the pressing matter of a likely decrease in learning skills based on the results of our study. The use of LLM had a measurable impact on participants, and while the benefits were initially apparent, as we demonstrated over the course of 4 months, the LLM group’s participants performed worse than their counterparts in the Brain-only group at all levels: neural, linguistic, scoring.
From the research paper:
Summary of results
We believe that some of the most striking observations in our study stem from Session 4, where Brain-to-LLM participants showed higher neural connectivity than LLM Group’s sessions 1, 2, 3 (network‑wide spike in alpha-, beta‑, theta‑, and delta-band directed connectivity). This suggests that rewriting an essay using AI tools (after prior AI-free writing) engaged more extensive brain network interactions. In contrast, the LLM-to-Brain group, being exposed to LLM use prior, demonstrated less coordinated neural effort in most bands, as well as bias in LLM specific vocabulary. Though scored high by both AI judge and human teachers, their essays stood out less in terms of the distance of NER/n-gram usage compared to other sessions in other groups. On the topic level, few topics deviated significantly and almost orthogonally (like HAPPINESS or PHILANTHROPY topics) in between LLM and Brain-only groups.
Conclusions
As we stand at this technological crossroads, it becomes crucial to understand the full spectrum of cognitive consequences associated with LLM integration in educational and informational contexts. While these tools offer unprecedented opportunities for enhancing learning and 142 information access, their potential impact on cognitive development, critical thinking, and intellectual independence demands a very careful consideration and continued research. The LLM undeniably reduced the friction involved in answering participants’ questions compared to the Search Engine. However, this convenience came at a cognitive cost, diminishing users’ inclination to critically evaluate the LLM’s output or ”opinions” (probabilistic answers based on the training datasets). This highlights a concerning evolution of the ‘echo chamber’ effect: rather than disappearing, it has adapted to shape user exposure through algorithmically curated content. What is ranked as “top” is ultimately influenced by the priorities of the LLM’s shareholders [123, 125]. Only a few participants in the interviews mentioned that they did not follow the “thinking” [124] aspect of the LLMs and pursued their line of ideation and thinking. Regarding ethical considerations, participants who were in the Brain-only group reported higher satisfaction and demonstrated higher brain connectivity, compared to other groups. Essays written with the help of LLM carried a lesser significance or value to the participants (impaired ownership, Figure 8), as they spent less time on writing (Figure 33), and mostly failed to provide a quote from theis essays (Session 1, Figure 6, Figure 7). Human teachers “closed the loop” by detecting the LLM-generated essays, as they recognized the conventional structure and homogeneity of the delivered points for each essay within the topic and group. We believe that the longitudinal studies are needed in order to understand the long-term impact of the LLMs on the human brain, before LLMs are recognized as something that is net positive for the humans.