Generative AI in Academic Settings: Exploring ChatGPT Adoption and Implications
Bibliographic record
Abstract
This research delves into the multifaceted aspects influencing the adoption and utilization of ChatGPT among postgraduate students. Executed as a qualitative study involving participants from a prominent federal university, the primary objective was to discern students’ nuanced perceptions of ChatGPT and their inclination to embrace it as an academic support tool. Based on Unified Theory of Acceptance and Use of Technology, the study employed thematic analysis with a focus on critical dimensions. Results illuminate that students are motivated to amplify academic performance, boost productivity, and streamline time management, with ChatGPT emerging as a user-friendly solution. Peer and faculty influence further solidified its integration, evolving into a habitual tool, and the availability of a free version significantly contributed to its widespread adoption. This research highlights the growing prevalence of ChatGPT across diverse academic activities, encompassing exploratory research, programming, presentations, and email composition—however, varying opinions surface regarding its efficacy and limitations in scientific text production. The research findings, particularly in the context of technology assimilation in academia, hold significant relevance for educational policymakers and practitioners. They offer valuable insights that can help form policies that foster the judicious and effective integration of technology within educational settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".