Artificial Intelligence considerations in nursing curricula: Embracing with care, its potential in transforming learning
Bibliographic record
Abstract
Artificial Intelligence (AI) has very quickly become a part of our daily lives, and nursing is no exception. Different types of AI are already becoming standard tools in nursing practice as healthcare comes to rely more on these powerful technologies to augment human capabilities. Nursing regulators and educators are realising that nurses will need new skills and competencies to use these tools safely, ethically, and responsibly in their practice. This paper explores important considerations that nursing educators must reflect on in the usage of AI and in particular, the unique conditions that they are expected to operate within. The paper will firstly introduce the current and emerging policy context of how AI is being constructed within healthcare and in particular what is emanating from a nursing education context. Secondly, the paper presents some overarching considerations that have emerged for nursing education following the rapid embedding of AI within nursing and wider healthcare delivery. Finally, we introduce a promising curricular framework, 'Constraints Led Approach' (CLA), the theoretical and applied foundations of which are in sport and skill acquisition, but which shows potential for structuring the inclusion of AI in nurse 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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".