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

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

Cognitive Load Theory (CLT), developed by John Sweller [30], provides a framework for understanding the mental effort required during learning and problem-solving. It identifies three categories of cognitive load: intrinsic cognitive load (ICL), which is tied to the complexity of the material being learned and the learner's prior knowledge; extraneous cognitive load (ECL), which refers to the mental effort imposed by presentation of information; and germane cognitive load (GCL), which is the mental effort dedicated to constructing and automating schemas that support learning. Sweller's research highlights that excessive cognitive load, especially from extraneous sources, can interfere with schema acquisition, ultimately reducing the efficiency of learning and problem-solving processes [30].

Cognitive load during LLM use

Cognitive load theory (CLT) allows us to better understand how LLMs affect learning outcomes. LLMs have been shown to reduce cognitive load across all types, facilitating easier comprehension and information retrieval compared to traditional methods like web searches [40]. LLM users experienced a 32% lower cognitive load compared to software-only users (those who relied on traditional software interfaces to complete tasks), with significantly reduced frustration and effort when finding information [41]. More specifically, given the three types of cognitive load, students using LLMs encountered the largest difference in germane cognitive load [40]. LLMs streamline the information presentation and synthesis process, thus reducing the need for active integration of information and in turn, a decrease in the cognitive effort required to construct mental schemas. This can be attributed to the concise and direct nature of LLM responses. A smaller decrease was seen for extraneous cognitive load during learning tasks [40]. By presenting targeted answers, LLMs reduce the mental effort associated with filtering through unrelated or extraneous content, which is usually a bearer of cognitive load when using traditional search engines. When CLT is managed well, users can engage more thoroughly with a task without feeling overwhelmed [41]. LLM users are 60% more productive overall and due to the decrease in extraneous cognitive load, users are more willing to engage with the task for longer periods, extending the amount of time used to complete tasks [41]. Although there is an overall reduction of cognitive load when using LLMs, it is important to note that this does not universally equate to enhanced learning outcomes. While lower cognitive loads often improve productivity by simplifying task completion, LLM users generally engage less deeply with the material, compromising the germane cognitive load necessary for building and automating robust schemas [40]. Students relying on LLMs for scientific inquiries produced lower-quality reasoning than those using traditional search engines, as the latter required more active cognitive processing to integrate diverse sources of information.

Additionally, it is interesting to note that the reduction of cognitive load leads to a shift from active critical reasoning to passive oversight. Users of GenAI tools reported using less effort in tasks such as retrieving and curating and instead focused on verifying or modifying AI-generated responses [42].

There is also a clear distinction in how higher-competence and lower-competence learners utilized LLMs, which influenced their cognitive engagement and learning outcomes [43]. Higher-competence learners strategically used LLMs as a tool for active learning. They used it to revisit and synthesize information to construct coherent knowledge structures; this reduced cognitive strain while remaining deeply engaged with the material. However, the lower-competence group often relied on the immediacy of LLM responses instead of going through the iterative processes involved in traditional learning methods (e.g. rephrasing or synthesizing material). This led to a decrease in the germane cognitive load essential for schema construction and deep understanding [43]. As a result, the potential of LLMs to support meaningful learning depends significantly on the user's approach and mindset.

Engagement during LLM use

Higher levels of engagement consistently lead to better academic performance, improved problem-solving skills, and increased persistence in challenging tasks [47]. Engagement encompasses emotional investment and cognitive involvement, both of which are essential to academic success. The integration of LLMs and multi-role LLM into education has transformed the ways students engage with learning, particularly by addressing the psychological dimensions of engagement. Multi-role LLM frameworks, such as those incorporating Instructor, Social Companion, Career Advising, and Emotional Supporter Bots, have been shown to enhance student engagement by aligning with Self-Determination Theory [48]. These roles address the psychological needs of competence, autonomy, and relatedness, fostering motivation, engagement, and deeper involvement in learning tasks. For example, the Instructor Bot provides real-time academic feedback to build competence, while the Emotional Supporter Bot reduces stress and sustains focus by addressing emotional challenges [48]. This approach has been particularly effective at increasing interaction frequency, improving inquiry quality, and overall engagement during learning sessions.

Personalization further enhances engagement by tailoring learning experiences to individual student needs. Platforms like Duolingo, with its new AI-powered enhancements, achieve this by incorporating gamified elements and real-time feedback to keep learners motivated [47]. Such personalization encourages behavioral engagement by promoting behavioral engagement (seen via consistent participation) and cognitive engagement through intellectual investment in problem-solving activities. Similarly, ChatGPT's natural language capabilities allow students to ask complex questions and receive contextually adaptive responses, making learning tasks more interactive and enjoyable [49]. This adaptability is particularly valuable in addressing gaps in traditional education systems, such as limited individualized attention and feedback, which often hinder active participation.

Despite their effectiveness in increasing the level of engagement across various realms, the sustainability of engagement through LLMs can be inconsistent [50]. While tools like ChatGPT and multi-role LLM are adept at fostering immediate and short-term engagement, there are limitations in maintaining intrinsic motivation over time. There is also a lack of deep cognitive engagement, which often translates into less sophisticated reasoning and weaker argumentation [49]. Traditional methods tend to foster higher-order thinking skills, encouraging students to practice critical analysis and integration of complex ideas.