AI-generated vs. student-crafted assignments and implications for evaluating student work in nursing: an exploratory reflection
Bibliographic record
Abstract
OBJECTIVES: Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial intelligence-powered language model that can generate a unique outputs, in reponse to a user's textual request. This has raised concerns related to academic integrity in nursing education as students may use the platform to generate original assignment content. Subsequently, the objective of this quality improvement project were to explore and identify effective strategies that educators can use to discern AI-generated papers from student-written submissions. METHODS: Four nursing students were requested to submit two versions a Letter to the Editor assignement; one assignment that was written by the student; the other, exclusively generated by ChatGPT-3.5. RESULTS: AI-generated assignments were typically grammatically well-written, but some of the scholarly references used were outdated, incorrectly cited, or at times completely fabricated,. Additionally, the AI-generated assignments lacked detail and depth. CONCLUSIONS: Nursing educators should possess an understanding of the capabilities of ChatGPT-like technologies to further enhance nursing students' knowledge development and to ensure academic integrity is upheld.
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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 | 0.049 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".