ChatGPT in Academic Writing: A Threat to Human Creativity and Academic Integrity? An Exploratory Study
Bibliographic record
Abstract
OpenAI ChatGPT has become the most popular academic writing software due to the kind of responses it gives, being seen as a replacement for much of the daily mundane writing, from emails to even college-style essays. As generative software, ChatGPT has caught the attention of everyone from business and policy stakeholders, signaling a paradigm shift in artificial intelligence. Despite ChatGPT’s popularity and appeal in academic writing, there are fears regarding its consequences for human creativity and academic integrity. The study employed critical literature review analysis to explore the importance of ChatGPT in academic writing, its effects on human creativity and academic integrity, and suggestions for proper adoption and application. In the review process, it was revealed that ChatGPT is important in improving the learning and academic outcomes of diverse professionals and learners. However, the review suggests that the responses or outputs from ChatGPT sometimes are inaccurate and misleading. Therefore, implications for policy and practice were discussed.
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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 | 0.035 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.006 |
| 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".