Original Article

A study on the artificial intelligence interview experience of nursing students in the COVID-19 situation

Abstract

Background & Aim: This study aims to examine the perception of artificial intelligence interviews experienced by prospective nursing graduates who have experienced artificial intelligence interviews at medical institutions using focus groups and provide necessary data to increase the efficiency of artificial intelligence interviews.
Methods & Materials: This study was conducted to examine nursing students' artificial intelligence interview experience during COVID-19 by performing a focus group interview and qualitative content analysis. The focus group interview was carried out on November 17, 2021, to understand nursing students' artificial intelligence interview experience during COVID-19, selecting a total of 14 senior nursing students.
Results: As a result of analyzing the artificial intelligence interview experiences of nursing students who participated in this study, 35 codes, grouped into eight subcategories, were derived. They are also classified into three categories 1) Finding your way in the dark, 2) Confronting artificial intelligence, and 3) Going beyond artificial intelligence. The eight subcategories derived are as follows: 1) Vagueness, 2) Find your way, 3) The fight between artificial intelligence and me, 4) Strong questions about interview evaluation, 5) New experience, 6) Learn your own tricks for artificial intelligence interviews, 7) Setting up the environment for artificial intelligence interview, 8) Establishment of information system for artificial intelligence interview.
Conclusion: Based on the results of this study, an educational program should be developed based on the main data obtained from the artificial intelligence interview experience so that nursing college students can adapt to the artificial intelligence interview.

1. Han CY, Park SA. The effect of the containment and closure policies on the spread of COVID-19. The Korea Association for Policy Studies. 2021;30(3):1-38. http://dx.doi.org/10.33900/KAPS.2021.30.3.1
2. Lee JH, Park MS, LEE SW. The Transmission dynamics of sars-cov-2 by setting in three waves in the seoul metropolitan area in South Korea. Health and Social Welfare Review. 2021 June;41(2):7-26. https://doi.org/10.15709/hswr.2021.41.2.7
3. Kang JY. Simulated nursing practice education in the ontact age: A mixed methods case study. The Journal of Learner-Centered Curriculum and Instruction. 2020 Sep;20(18):937-957. DOI: http://doi.org/10.2251/jlci.2020.20.18.937
4. Korea Disease Control and Prevention Agency. Chungbuk: Author. Available from: http://ncov.mohw.go.kr/tcmBoardView.do?brdId＝3&brdGubun＝31&dataGubun＝&ncvContSeq＝6242&contSeq＝6242&board_id＝312&gubun＝ALL. Accessed December 4, 2021
5. Morris KC, Schlenoff C, Srinivasan V. A remarkable resurgence of artificial intelligence and its impact on automation and autonomy. IEEE Transactions on Automation Science and Engineering. 2017 Apr;14(2):407-9. doi: 10.1109/TASE.2016.2640778.
6. Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Artificial Intelligence and Machine Learning in Anesthesiology. 2019 Dec;131(6):1346-59. doi: 10.1097/ALN.0000000000002694.
7. Siobhan O'Connor. Artificial intelligence and predictive analytics in nursing education. Nurse Education in Practice. 2021 Oct;56:103224. DOI: 10.1016/j.nepr.2021.103224
8. Yang HT. Safety Issues of Artificial Intelligence and Policy Responses. The Journal of Korean Institute of Communications and Information Sciences. 2018 Oct;43(10):1724-32. https://doi.org/10.7840/kics.2018.43.10.1724
9. Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. Journal of medical Internet research. 2021 Nov;23(11):e26522. doi: 10.2196/26522.
10. Baek SJ, Choi SH, Ryu YH, et al. Developing Interview Tool for Selecting Applicants with Aptitude for Their Major in Undergraduate Admission Assessment. The Journal of Modern Social Science Research. 2015 Dec;20(0): 1-29.
11. Lee BS, Eo YS, Lee MA. Leadership experience of clinical nurses: applying focus group interviews. Journal of Korean Academy of Nursing. 2015 Oct 1;45(5):671-83.
12. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qualitative health research. 2005 Nov;15(9):1277-88. https://doi.org/10.1177/1049732305276687
13. Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Education Today. 2004 Feb;24(2):105-12. doi: 10.1016/j.nedt.2003.10.001.
14. Shin NM, Chang SJ. A Study on High School Students' Perceptions of AI Interview for University Admission. The Journal of the Korea Academia-Industrial cooperation Society. 2021 Jul;22(7): 242-51. Doi: https://doi.org/10.5762/KAIS.2021.22.7.242
15. Gray JR, Grove SK, Sutherland S. Burns and grove's the practice of nursing research-E-book: Appraisal, synthesis, and generation of evidence. Elsevier Health Sciences; 2016 Aug 10.
16. Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, Cato K, Hardiker N, Junger A, Michalowski M, Nyrup R. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative. Journal of advanced nursing. 2021 Sep;77(9):3707-17.
17. Wilson R, Godfrey CM, Sears K, Medves J, Ross-White A, Lambert N. Exploring conceptual and theoretical frameworks for nurse practitioner education: a scoping review protocol. JBI Evidence Synthesis. 2015 Oct 1;13(10):146-55.
18. Cheng SF. Transformation in Nursing Education: Development and Implementation of Diverse Innovative Teaching. Journal of Nursing. 2021 Dec;68(6):4-5. doi: 10.6224/JN.202112_68(6).01.
19. Gray K, Slavotinek J, Dimaguila GL, Choo D. Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs. JMIR medical education. 2022 Apr 4;8(2):e35223. doi: 10.2196/35223.
20. Kim YH. Effect of career empowerment program on career maturity, career decision-making self-efficacy, and employment stress of nursing college students. The Journal of the Korea Contents Association. 2013;13(12):817-28.
21. Byun SY. A Study on the Problem of AI Bias in Data Ethics. The Korean Journal of Ethics. 2020 Mar;1(128):148-58. DOI: 10.15801/je.1.128.202003.143
22. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR nursing. 2021 Jan 28;4(1):e23933. doi: 10.2196/23933. eCollection Jan-Mar 2021.
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IssueVol 9 No 3 (2022): Summer QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/npt.v9i3.10224
Keywords
artificial intelligence; experience; students; nursing
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Young Park S, Park M, Young Choi N, Park S. A study on the artificial intelligence interview experience of nursing students in the COVID-19 situation. NPT. 2022;9(3):221-233.