Origin: Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

Doi：10.48550/arXiv.2506.08872

Abstract：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, 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.

We hope this study serves as a preliminary guide to understanding the cognitive and practical

impacts of AI on learning environments.