Exploring the Adherence to AI-Generated Writing Standards: Practice Levels among University Students
Bibliographic record
Abstract
Since writing was done with a quill, then with typewriters, and up to modern text processing programs, writing has always adhered to standards concerning the responsibilities and roles of the author, which cannot be abdicated. Given that adherence to writing standards may be a suitable way to regulate the use of generative artificial intelligence, this study explores the extent to which a sample of university students adhere to the standards of AI-generated writing. A total of 326 students majoring in the humanities participated in the study. The research was designed as a quantitative study, employing a descriptive-analytical approach to process the obtained results. A proposed list of standards for AI-generated writing suitable for undergraduate students was prepared and presented to experts. Based on the proposed standards list, data were collected using a graduated practice scale that illustrates the levels of student adherence to AI-generated writing standards. The scaled measure delved into student practices related to standards such as Input Guidance, Disciplined Dependability, Content Validation, Critical Analysis, Documentation, Review and Editing, and Responsibility. Students achieved beginner and intermediate ratings in their adherence to AI-generated writing standards, and they attained very low levels in the advanced practices of standard. This paper contributes to our understanding of how students apply AI-generated writing standards. The gap revealed by the current study's results prompts us to establish regulatory guidelines to enforce student adherence to AI-generated writing standards.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".