Nursing Students’ Perceptions of Using ChatGPT in a Written Assignment
Bibliographic record
Abstract
Introduction: The availability and use of artificial intelligence (AI) tools is accelerating significantly. As these technologies proliferate, many post-secondary institutions have responded by banning students from using AI tools such as ChatGPT and framing the conversation as breaches of academic integrity. Background: Despite these institutional responses, many students adopt these tools as part of their learning journey. In health care settings, the adoption of such tools in the context of patient care provision is a reality. Consequently, there is a relevant pedagogical opportunity to examine how such tools inform the experiential learning of nursing students and their future practice. Methods: To address the dearth of information regarding nursing students’ perceptions of using AI tools, a Canadian university teaching team incorporated ChatGPT into an undergraduate nursing course assignment. A pilot quasi-experimental pre-post-test survey design was employed to examine student perceptions of using ChatGPT. After obtaining institutional ethics approval, a neutral third party collected the anonymous data. Findings: Pilot study results highlighted significant student concerns regarding the ethics of using AI tools. Additionally, students described such tools as meaningful avenues to support learning access and equity. Finally, students identified a high probability of use of AI tools in their future practice, suggesting that exposure and support during learning can positively influence responses to these tools in practice settings. Conclusion: The students surveyed are now practising nurses; thus, findings may provide insight into perceptions of new nurses regarding the integration of AI to support competencies required by the nurses of tomorrow.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".