Artificial intelligence in advanced practice nursing education: Opportunities and challenges
Bibliographic record
Abstract
ABSTRACT: Artificial intelligence (AI) is transforming health care delivery and education, offering both opportunities and challenges. Because AI tools become more integrated into clinical practice, educators are exploring their potential to enhance learning, improve decision making, and prepare future providers for technology-rich environments. However, the use of AI in health care education raises concerns about academic integrity, overreliance, and the risk of AI-generated misinformation, known as "hallucinations." These inaccuracies pose significant challenges for students building foundational knowledge. Additionally, practical issues such as implementation costs, Health Insurance Portability and Accountability Act compliance, data privacy, and inherent biases in AI systems must be addressed. This article discusses the integration of AI into advanced practice nursing education using the Technological Pedagogical Content Knowledge (TPACK) framework. This framework emphasizes the interconnectedness of technology, pedagogy, and content knowledge, guiding educators in designing assignments that promote critical thinking and prepare students for real-world clinical practice. The evolution of an AI-based assignment for advanced practice nursing students is detailed, highlighting ethical considerations, interdisciplinary collaboration, and the development of clinical reasoning skills. By integrating AI into nursing education, we can ensure that advanced practice registered nurses (APRNs) are well-prepared to meet the demands of modern health care environments. This integration supports essential competencies, aligns with the 2021 American Association of Colleges of Nursing Essentials, and addresses the increasing complexity of patient care. Moreover, it equips APRNs with the skills needed to leverage AI effectively, fostering a culture of continuous learning and adaptation.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| 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".