This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM,
Excerpts:
"The convenience of instant answers that LLMs provide can encourage passive consumption of information, which may lead to superficial engagement, weakened critical thinking skills, less deep understanding of the materials, and less long-term memory formation [8]. The reduced level of cognitive engagement could also contribute to a decrease in decision-making skills and in turn, foster habits of procrastination and "laziness" in both students and educators [13].
Additionally, due to the instant availability of the response to almost any question, LLMs can possibly make a learning process feel effortless, and prevent users from attempting any independent problem solving. By simplifying the process of obtaining answers, LLMs could decrease student motivation to perform independent research and generate solutions [15]. Lack of mental stimulation could lead to a decrease in cognitive development and negatively impact memory [15]. The use of LLMs can lead to fewer opportunities for direct human-to-human interaction or social learning, which plays a pivotal role in learning and memory formation [16].
Collaborative learning as well as discussions with other peers, colleagues, teachers are critical for the comprehension and retention of learning materials. With the use of LLMs for learning also come privacy and security issues, as well as plagiarism concerns (7]. Yang et al. [17] conducted a study with high school students in a programming course. The experimental group used ChatGPT to assist with learning programming, while the control group was only exposed to traditional teaching methods. The results showed that the experimental group had lower flow experience, self-efficacy, and learning performance compared to the control group.
Academic self-efficacy, a student's belief in their "ability to effectively plan, organize, and execute academic tasks"
', also contributes to how LLMs are used for learning [18]. Students with
low self-efficacy are more inclined to rely on Al, especially when influenced by academic stress
[18]. This leads students to prioritize immediate Al solutions over the development of cognitive and creative skills. Similarly, students with lower confidence in their writing skills, lower
"self-efficacy for writing" (SEWS), tended to use ChatGPT more extensively, while higher-efficacy students were more selective in Al reliance [19]. We refer the reader to the meta-analysis [20] on the effect of ChatGPT on students' learning performance, learning perception, and higher-order thinking."
"Recent empirical studies reveal concerning patterns in how LLM-powered conversational search systems exacerbate selective exposure compared to conventional search methods. Participants engaged in more biased information querying with LLM-powered conversational search, and an opinionated LLM reinforcing their views exacerbated this bias [63]. This occurs because LLMS are in essence "next token predictors" that optimize for most probable outputs, and thus can potentially be more inclined to provide consonant information than traditional information system algorithms [63]. The conversational nature of LLM interactions compounds this effect, as users can engage in multi-turn conversations that progressively narrow their information exposure. In LLM systems, the synthesis of information from multiple sources may appear to provide diverse perspectives but can actually reinforce existing biases through algorithmic selection and presentation mechanisms.
The implications for educational environments are particularly significant, as echo chambers can fundamentally compromise the development of critical thinking skills that form the foundation of quality academic discourse. When students rely on search systems or language models that systematically filter information to align with their existing viewpoints, they might miss opportunities to engage with challenging perspectives that would strengthen their analytical capabilities and broaden their intellectual horizons. Furthermore, the sophisticated nature of these algorithmic biases means that a lot of users often remain unaware of the information gaps in their research, leading to overconfident conclusions based on incomplete evidence. This creates a cascade effect where poorly informed arguments become normalized in academic and other settings, ultimately degrading the standards of scholarly debate and undermining the educational mission of fostering independent, evidence-based reasoning."
"In summary, the Brain-only group's connectivity suggests a state of increased internal coordination, engaging memory and creative thinking (manifested as theta and delta coherence across cortical regions). The Engine group, while still cognitively active, showed a tendency toward more focal connectivity associated with handling external information (e.g. beta band links to visual-parietal areas) and comparatively less activation of the brain's long-range memory circuits. These findings are in line with literature: tasks requiring internal memory amplify low-frequency brain synchrony in frontoparietal networks [77], whereas outsourcing information (via internet search) can reduce the load on these networks and alter attentional dynamics. Notably, prior studies have found that practicing internet search can reduce activation in memory-related brain areas [831, which dovetails with our observation of weaker connectivity in those regions for Search Engine group. Conversely, the richer connectivity of Brain-only group may reflect a cognitive state akin to that of high performers in creative or memory tasks, for instance, high creativity has been associated with increased fronto-occipital theta connectivity and intra-hemispheric synchronization in frontal-temporal circuits [81], patterns we see echoed in the Brain-only condition."
"This correlation between neural connectivity and behavioral quoting failure in LLM group's participants offers evidence that:
1. Early Al reliance may result in shallow encoding.
LLM group's poor recall and incorrect quoting is a possible indicator that their earlier essays were not internally integrated, likely due to outsourced cognitive processing to the LLM.
2. Withholding LLM tools during early stages might support memory formation.
Brain-only group's stronger behavioral recall, supported by more robust EEG connectivity, suggests that initial unaided effort promoted durable memory traces, enabling more effective reactivation even when LLM tools were introduced later.
Metacognitive engagement is higher in the Brain-to-LLM group.
Brain-only group might have mentally compared their past unaided efforts with tool-generated suggestions (as supported by their comments during the interviews), engaging in self-reflection and elaborative rehearsal, a process linked to executive control and semantic integration, as seen in their EEG profile.
The significant gap in quoting accuracy between reassigned LLM and Brain-only groups was not merely a behavioral artifact; it is mirrored in the structure and strength of their neural connectivity. The LLM-to-Brain group's early dependence on LLM tools appeared to have impaired long-term semantic retention and contextual memory, limiting their ability to reconstruct content without assistance. In contrast, Brain-to-LLM participants could leverage tools more strategically, resulting in stronger performance and more cohesive neural signatures."
