Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Comparative Analysis of NCLEX-RN Questions: A Duel Between ChatGPT and Human Expertise
25
Zitationen
3
Autoren
2023
Jahr
Abstract
Background: Artificial intelligence (AI) has the potential to revolutionize nursing education. This study compared NCLEX-RN questions generated by AI and those created by nurse educators. Method: Faculty of accredited baccalaureate programs were invited to participate. Likert-scale items for grammar and clarity of the item stem and distractors were compared using Mann-Whitney U, and yes/no questions about clinical relevance and complex terminology were analyzed using chi-square. A one-sample binomial test with confidence intervals evaluated participants' question preference (AI-generated or educator-written). Qualitative responses identified themes across faculty. Results: Item clarity, grammar, and difficulty were similar for AI and educator-created questions. Clinical relevance and use of complex terminology was similar for all question pairs. Of the four sets with preference for one item, three were generated by AI. Conclusion: AI can assist faculty with item generation to prepare nursing students for the NCLEX-RN examination. Faculty expertise is necessary to refine questions written using both methods. [ J Nurs Educ . 2023;62(12):679–687.]
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.774 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.685 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.244 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.898 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.