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Comparative Analysis of NCLEX-RN Questions: A Duel Between ChatGPT and Human Expertise.

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Title: Comparative Analysis of NCLEX-RN Questions: A Duel Between ChatGPT and Human Expertise.
Authors: Cox, Rachel L. (NURSE) rlcox@mghihp.edu, Hunt, Karen L. (NURSE), Hill, Rebecca R. (NURSE)
Source: Journal of Nursing Education. Dec2023, Vol. 62 Issue 12, p679-687. 9p.
Subject Terms: *Computer assisted testing (Education), *Research methodology evaluation, *Nursing schools, *National Council Licensure Examination for Registered Nurses, *Artificial intelligence, *Baccalaureate nursing education, *Comparative grammar, *Comparative studies, *Qualitative research, *Evaluation, Confidence intervals, Mann Whitney U Test, Nurses, Descriptive statistics, Chi-squared test, Scale analysis (Psychology), Thematic analysis
Geographic Terms: New England
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.] [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
Description
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.] [ABSTRACT FROM AUTHOR]
ISSN:01484834
DOI:10.3928/01484834-20231006-07