In recent years, the rise of large language models (LLMs), especially conversational systems such as GPT, has transformed the way students learn, search, and construct knowledge. These tools provide immediate access to information, linguistic support, and problem-solving guidance. Yet, the growing reliance on AI systems raises a critical question: does dialogue with GPT signify an augmentation of learning, or does it risk the erosion of students’ independent cognitive engagement?
This paper situates itself at the intersection of educational technology and human-computer interaction (HCI), focusing on the dual nature of AI in shaping student learning behaviors. By analyzing authentic dialogue data between students and GPT, combined with cognitive load assessments and qualitative insights, this research explores how students negotiate efficiency, dependency, and autonomy. The findings illuminate the paradox of “dialogue as learning”—while GPT reduces cognitive barriers, it simultaneously fosters varying levels of reliance that reconfigure the learning process.
Cognitive Load Theory (CLT), pioneered by Sweller (1988), emphasizes that learning efficiency depends on balancing intrinsic, extraneous, and germane load. Educational technologies can reduce extraneous load by simplifying information access. GPT offers instant answers and structured explanations, which can reduce search burden and enhance germane processing. However, the vast generative capacity of LLMs also risks introducing redundant information, thereby increasing extraneous load (Paas & van Merriënboer, 2020).
Recent research (Mayer, 2021) has indicated that adaptive technologies must align with cognitive architecture to avoid overloading working memory. GPT’s adaptive responses represent a potential double-edged sword: supporting novice learners while simultaneously overwhelming them with complexity.
Automation dependency, a term rooted in HCI and aviation research (Parasuraman & Riley, 1997), describes the phenomenon in which users increasingly rely on automated systems, even when errors occur. In educational contexts, students using GPT may gradually outsource reasoning tasks, developing a reliance that undermines critical thinking. Studies on intelligent tutoring systems (Koedinger & Aleven, 2016) show that while scaffolding improves learning outcomes, excessive automation diminishes self-regulation. GPT-based interactions represent an unprecedented scale of this tension, given the system’s fluency and anthropomorphic conversational style.
Scholars have documented multiple benefits of AI integration in classrooms: personalized tutoring, real-time feedback, and enhanced writing support (Holmes et al., 2019). For instance, GPT’s ability to reformulate complex texts into digestible summaries may expand accessibility for diverse learners. Yet, there is growing critique of the “AI crutch effect,” where students bypass effortful problem-solving in favor of generated solutions (Luckin, 2021).
Comparative studies (Motlagh et al., 2023) highlight that while GPT outperforms earlier models such as MPT-7b and Falcon-7b in fluency and coherence, it also exacerbates dependency risks due to its higher credibility. Therefore, the interplay between efficiency and autonomy remains a pressing research frontier.
Dialogic theories of learning (Bakhtin, 1981; Wegerif, 2019) argue that knowledge construction occurs through dialogue, negotiation, and co-construction of meaning. GPT’s conversational mode reframes human-computer interaction as dialogic learning, where students not only consume but also co-shape knowledge. Yet, the asymmetry between human agency and machine generation raises questions: when does dialogue foster metacognition, and when does it entrench passive reliance? This conceptual dilemma anchors the present study.
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This study adopts a mixed-methods design, combining quantitative cognitive load measurement with qualitative dialogue analysis. The aim is to capture both the measurable cognitive impact of GPT interaction and the nuanced dependency patterns emerging in student behaviors.
A purposive sample of 60 university students was recruited from two disciplines—humanities (n=30) and STEM (n=30). The group included undergraduates and master’s students to capture developmental variation. Participants had prior but uneven exposure to GPT, ensuring diversity in familiarity.
Dialogue Data: Over 20,000 interaction turns between students and GPT were collected during three-week academic tasks, including essay writing, problem-solving, and literature review assignments.
Cognitive Load Measures:
Paas Cognitive Load Rating Scale (9-point Likert).
Eye-tracking experiments to measure fixation duration and regressions during GPT consultation.
Survey and Interviews: Post-task surveys assessed perceived dependency, while semi-structured interviews (n=20) probed students’ attitudes toward GPT as a learning companion.
Conversation Analysis (CA): Identified turn-taking strategies, repair sequences, and epistemic stance shifts.
Cognitive Load Analysis: Quantitative results analyzed through ANOVA to compare across disciplines and task complexity.
Dependency Mechanism Modeling: Grounded Theory was employed to inductively derive categories of dependency behaviors.
Triangulation across dialogue logs, cognitive measures, and interviews ensured validity. Inter-coder reliability for qualitative coding exceeded 0.85 (Cohen’s kappa). Ethical approval was obtained, and data anonymized.
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Quantitative results reveal discipline-specific variations. Humanities students reported lower extraneous load with GPT assistance, while STEM students exhibited higher germane load, engaging GPT for exploratory reasoning. Eye-tracking confirmed reduced regressions in text comprehension with GPT support. However, novices showed cognitive underload, relying on GPT’s answers without processing.
Analysis uncovered a three-stage dependency trajectory:
Immediate Resolution – Students used GPT as a “solution engine,” rapidly extracting answers.
Progressive Reliance – Over time, participants reduced independent search and problem-solving, displaying automation dependency.
Critical Adjustment – A subset of advanced learners questioned GPT outputs, cross-validating with external sources.
This trajectory aligns with automation theory, yet highlights the educational potential of “critical adjustment” as a teachable competency.
CA revealed that students often positioned GPT as an epistemic authority, using confirmatory questions such as “Is this correct?” rather than exploratory prompts. Dependency intensified when GPT presented answers with high linguistic confidence. Conversely, when GPT provided hedged or uncertain responses, students were more likely to re-engage in critical reasoning.
The paradox of GPT interaction is clear: while it reduces extraneous load and accelerates task completion, it risks undermining autonomy if left unmoderated. Educators must therefore integrate prompt literacy training, guiding students to frame exploratory, open-ended queries rather than answer-seeking ones. Furthermore, institutional policies should encourage “AI transparency,” requiring students to document how GPT contributed to their work.
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This study demonstrates that GPT interaction reshapes cognitive and behavioral dimensions of student learning. Dialogue with GPT is not merely instrumental but reconstructive, mediating both cognitive load and dependency trajectories. The findings highlight a dual imperative: to harness GPT’s capacity to reduce barriers to learning while preventing overreliance that diminishes autonomy.
For future research, cross-cultural studies and longitudinal designs are needed to examine how dependency evolves over time and across educational systems. Ultimately, the principle of “AI as collaborator, not substitute” must guide both technological design and pedagogical practice.
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Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
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Motlagh, N. Y., Khajavi, M., Sharifi, A., & Ahmadi, M. (2023). Comparative study of text generation tools in digital education. International Journal of Educational Technology in Higher Education, 20(1), 1–19.
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Wegerif, R. (2019). Dialogic Education: Mastering Core Concepts Through Thinking Together. Routledge.
