Undergraduate Nursing Students’ Perspectives on Artificial Intelligence in Academia
Bibliographic record
Abstract
With Artificial Intelligence (AI) tools becoming increasingly commonplace, the usage of AI-enabled tools in education has also grown. AI-enabled tools refer to machines incorporated with human-like capabilities, such as reasoning, interpretation, and problem-solving, to perform tasks that require human intelligence. ChatGPT is one of these tools, which uses large language models (LLM), a type of AI that generates natural language, to give human-like answers to questions. This study investigated nursing students' perspectives on AI-enabled tools, such as ChatGPT, aiming to identify (1) perceived benefits and challenges and (2) implications for the ethical and responsible use of AI within undergraduate nursing programs. Using interpretive description, we conducted focus group interviews with undergraduate nursing students. Through convenience sampling, sixteen students were recruited. Our findings revealed four key themes - utilization as a support tool, utilization leading to a loss of competency in foundational skills, utilization risking credibility and academic integrity, and the need for further education and resources. Three key factors - evidence-based practice, ethical considerations, and the importance of critical thinking skills - influence nursing students' perspectives toward AI tools. To ensure the safe and ethical use of AI in academia, robust institutional policies and training are needed. Promoting open dialogues and education can help students understand AI's advantages, potential harms, and risk mitigation strategies. Future research should build a comprehensive understanding of the perspectives of undergraduate and graduate nursing students, and educators on AI usage in academia. Development of interventions that mitigate AI-usage risks is also necessary to improve integration into education.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".