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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]
Copyright of Journal of Nursing Education is the property of SLACK Incorporated and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Comparative Analysis of NCLEX-RN Questions: A Duel Between ChatGPT and Human Expertise.
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  Data: <searchLink fieldCode="AR" term="%22Cox%2C+Rachel+L%2E%22">Cox, Rachel L.</searchLink> (NURSE)<i> rlcox@mghihp.edu</i><br /><searchLink fieldCode="AR" term="%22Hunt%2C+Karen+L%2E%22">Hunt, Karen L.</searchLink> (NURSE)<br /><searchLink fieldCode="AR" term="%22Hill%2C+Rebecca+R%2E%22">Hill, Rebecca R.</searchLink> (NURSE)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Nursing+Education%22">Journal of Nursing Education</searchLink>. Dec2023, Vol. 62 Issue 12, p679-687. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Computer+assisted+testing+%28Education%29%22">Computer assisted testing (Education)</searchLink><br />*<searchLink fieldCode="DE" term="%22Research+methodology+evaluation%22">Research methodology evaluation</searchLink><br />*<searchLink fieldCode="DE" term="%22Nursing+schools%22">Nursing schools</searchLink><br />*<searchLink fieldCode="DE" term="%22National+Council+Licensure+Examination+for+Registered+Nurses%22">National Council Licensure Examination for Registered Nurses</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Baccalaureate+nursing+education%22">Baccalaureate nursing education</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+grammar%22">Comparative grammar</searchLink><br />*<searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br />*<searchLink fieldCode="DE" term="%22Qualitative+research%22">Qualitative research</searchLink><br />*<searchLink fieldCode="DE" term="%22Evaluation%22">Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Mann+Whitney+U+Test%22">Mann Whitney U Test</searchLink><br /><searchLink fieldCode="DE" term="%22Nurses%22">Nurses</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Chi-squared+test%22">Chi-squared test</searchLink><br /><searchLink fieldCode="DE" term="%22Scale+analysis+%28Psychology%29%22">Scale analysis (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+analysis%22">Thematic analysis</searchLink>
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  Data: 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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  Data: <i>Copyright of Journal of Nursing Education is the property of SLACK Incorporated and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=173992700
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        Value: 10.3928/01484834-20231006-07
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      – SubjectFull: Computer assisted testing (Education)
        Type: general
      – SubjectFull: Research methodology evaluation
        Type: general
      – SubjectFull: Nursing schools
        Type: general
      – SubjectFull: National Council Licensure Examination for Registered Nurses
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Baccalaureate nursing education
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      – SubjectFull: Nurses
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      – SubjectFull: New England
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      – TitleFull: Comparative Analysis of NCLEX-RN Questions: A Duel Between ChatGPT and Human Expertise.
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              Text: Dec2023
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