Developers using AI showed the same core problem-solving behaviors as those without but differed in how they became stuck and recovered, with AI helping or hindering in specific cases.
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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
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This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
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NIRVANA supplies keystroke-level logs, complete ChatGPT dialogues, and copied content from 77 students to reconstruct AI-assisted essay writing and classify students into four behavioral profiles: Lead Authors, Collaborators, Drafters, and Vibe Writers.
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
Critical Inker scaffolds critical reflection during AI-assisted writing via Socratic questioning and visual logical-error feedback, reporting 91.2% argument overlap with ground truth and 87% validity accuracy in a pilot evaluation.
RelianceScope is a new analytical framework that maps AI reliance into nine engagement patterns across help-seeking and response-use, situated in students' prior knowledge and instructional context, validated on programming course logs.
TaskLens uses LLMs to generate task-specific scaffolded interfaces that reduce perceived workload and improve performance and concept learning for novices using professional 3D software.
Introduces the Techno-Supremacy Doctrine as an analytical framework and finds that AI executive discourse shows polarization with a general increase in pro-technology-solution narratives after ChatGPT, often acknowledging risks only to advocate further tech development.
Large-scale topic modeling of 270k Reddit posts shows GenAI discourse in education shifting from detection-evasion to enforcement, with K-12 teachers emphasizing cognitive dependency, academics focusing on detection, students on career anxiety, and adversarial themes driving engagement and cross-sta
Mixed-methods study finds AI assistance linked to higher textual overlap with suggestions in writing tasks, and a reflective interface prototype increases user awareness of AI incorporation.
A minimal three-variable dynamical model of human-AI feedback predicts that increasing reliance on AI induces a transition to a low-diversity suboptimal equilibrium, interpreted as an emergent information bottleneck.
Claude Code centers on a model-tool while-loop surrounded by permission systems, context compaction, extensibility hooks, subagent delegation, and session storage; the same design questions yield different answers in OpenClaw's gateway context.
Interviews with 22 developers produced a preliminary reliance-control framework that uses levels of control over AI to identify appropriate reliance in software engineering.
Agentic entropy names the systemic drift in AI coding agents away from architectural intent; a new framework using conformity seeding, reasoning monitoring, and causal graph interfaces supplies process-level oversight to complement existing review methods.
Trust-driven routine use of generative AI is linked to reduced cognitive engagement in STEM students, with higher technophilic traits increasing vulnerability.
VizCopilot integrates topic modeling with document visualization to support user oversight of retrieved context in enterprise chatbots, enabling detection of misalignments and adaptation of prompting strategies.
Interviews reveal a four-stage vibe coding workflow that accelerates prototyping while introducing tensions between quick efficiency and reflective design intention, plus asymmetries in trust and ownership.
AI argumentative feedback on community notes produces larger quality improvements than supportive or neutral feedback in a hybrid moderation experiment.
An online study of 70 students found that gender, race, and self-efficacy predict distinct ChatGPT query patterns during essay writing, with patterns linked to enjoyment and perceived ownership of the final essay.
This position paper advocates shifting AI education in materials discovery from basic tool access to a workflow-aligned literacy model that builds scientific judgment and equitable outcomes.
Prober.ai constrains LLMs via personas and JSON schemas to deliver gated, inquiry-based questions on argumentative writing weaknesses, aiming to reduce cognitive debt from AI overuse.
Advanced LLMs improve EFL writing scores and diversity for lower-proficiency students but correlate with lower expert ratings on deep coherence, acting more as crutches than scaffolds.
AI functions as a determinant of health with ambient and personal exposure types, requiring new epidemiological study designs beyond current experiments.
Student-facilitated workshops in one design class produced AI policies highlighting double standards in disclosure requirements between students and faculty, demonstrating value in participatory governance.
A pilot mixed-methods study at one university uses surveys and pre/post-LLM grade data to document patterns in faculty course design and student learning outcomes after generative AI release.
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An Empirical Study to Understand How Students Use ChatGPT for Writing Essays
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