Exploring new frontiers in nursing education: Assessing the role of generative AI (chat GPT) in aligning family nurse practitioner coursework to AACN’s new essentials
Bibliographic record
Abstract
Background and purpose: Integrating Generative Artificial Intelligence (Gen AI) in higher education, specifically within health sciences, is increasingly recognized for its potential to enhance educational outcomes and efficiency. The American Association of Colleges of Nursing (AACN) mandates the alignment of Family Nurse Practitioner (FNP) programs with its 2021 Essentials, a competency-based educational framework encompassing hundreds of specific standards. This study aims to evaluate a novel use of Gen AI: how effectively can a custom-trained gen AI tool (custom GPT from ChatGPT), align FNP course assessments with the AACN’s New Essentials, thereby potentially reducing faculty workload and improving curriculum accuracy.Methods: Through dialogue and uploading of relevant documents, a custom GPT (called Mapper) was trained from one FNP course to the subcompentencies within the 2021 Essentials. The Mapper was then used to align the assessments from one FNP course. The Mapper’s output was then compared to content expert alignments to assess accuracy.Results: Across all 10 domains of the AACN Essentials, the Mapper aligned with expert analysis with moderate to high accuracy. Initial analysis indicated correct alignment rate from 44% to 93% (average 66%), which improved to 70% (p < .05) upon further refinement of the Mapper tool by content expert. Potential novel alignments (average 26%), and misalignments (average 9%), provided by the Mapper were critically reviewed, leading to adjustments to the content expert’s original alignment, which enhanced the overall precision of the alignment. For example, misalignments were reduced to only 5% (p < .05). In post-analysis, Mapper aligned AACN subcompetencies incorrectly on average 4%, while the lead faculty was incorrect on average 6%.Conclusions: Gen AI has the potential to streamline the complex process of aligning curriculum to national standards. The GPT demonstrated a significant capacity to assist in this task with minimal error rates, but expert oversight remained crucial to ensure accuracy and relevance. This synergy between Gen AI and human expertise points to a promising avenue for enhancing curriculum development and alignment processes in nursing education and other disciplines.
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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 | 0.027 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".