CoChat
Sign Up
Cited by 91Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

A Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of ChatGPT Integration in Nursing Education: A Narrative Review

Open access
Abujaber, Ahmad A, Abd-alrazaq, Alaa, Al-Qudimat, Ahmad R, Nashwan, Abdulqadir J·2023·Cureus
DOI 10.7759/cureus.48643PMC PMC10711690
Open this paper in CoChat — free accountOpen PDF

Abstract

Amidst evolving healthcare demands, nursing education plays a pivotal role in preparing future nurses for complex challenges. Traditional approaches, however, must be revised to meet modern healthcare needs. The ChatGPT, an AI-based chatbot, has garnered significant attention due to its ability to personalize learning experiences, enhance virtual clinical simulations, and foster collaborative learning in nursing education. This review aims to thoroughly assess the potential impact of integrating ChatGPT into nursing education. The hypothesis is that valuable insights can be provided for stakeholders through a comprehensive SWOT analysis examining the strengths, weaknesses, opportunities, and threats associated with ChatGPT. This will enable informed decisions about its integration, prioritizing improved learning outcomes. A thorough narrative literature review was undertaken to provide a solid foundation for the SWOT analysis. The materials included scholarly articles and reports, which ensure the study's credibility and allow for a holistic and unbiased assessment. The analysis identified accessibility, consistency, adaptability, cost-effectiveness, and staying up-to-date as crucial factors influencing the strengths, weaknesses, opportunities, and threats associated with ChatGPT integration in nursing education. These themes provided a framework to understand the potential risks and benefits of integrating ChatGPT into nursing education. This review highlights the importance of responsible and effective use of ChatGPT in nursing education and the need for collaboration among educators, policymakers, and AI developers. Addressing the identified challenges and leveraging the strengths of ChatGPT can lead to improved learning outcomes and enriched educational experiences for students. The findings emphasize the importance of responsibly integrating ChatGPT in nursing education, balancing technological advancement with careful consideration of associated risks, to achieve optimal outcomes.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.7759/cureus.48643

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A coherent synthesis of emerging evidence on GenAI in nursing education is lacking.

    “However, a coherent synthesis of this emerging evidence is lacking.”

    From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Abujaber et al., 2023

    The Review’s Methods section says the authors conducted an exhaustive narrative literature review and synthesized findings about ChatGPT in nursing education under SWOT themes. Rather than saying a synthesis was lacking, the cited paper describes carrying one out. The passages have no page numbers in our copy. (LLM, conf=0.78)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • Limited hands-on evaluation methods and overreliance risks further challenge effective implementation.

    “Limited hands-on evaluation methods and overreliance risks further challenge effective implementation”

    From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Abujaber et al., 2023

  • Abujaber et al. (2023) used SWOT analysis to identify strengths and threats associated with ChatGPT without mapping implementation patterns across educational contexts.

    “Abujaber et al. (2023) used SWOT analysis to identify strengths and threats associated with ChatGPT without mapping implementation patterns across educational contexts.”

    From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Abujaber et al., 2023

  • Most existing reviews of GenAI in nursing education lack structured analytical frameworks for systematically mapping evidence into actionable insights.

    “A few reviews have begun to examine GenAI in nursing education, but most lack structured analytical frameworks for systematically mapping evidence into actionable insights.”

    From Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review (2025) · cites this paper as Abujaber et al., 2023

    The Introduction and background and Review > Methods sections describe this paper’s own SWOT framework and its synthesis of prior literature. They do not assess how many other GenAI-in-nursing-education reviews exist or whether most lack structured frameworks. The Review > Limitations section discusses limitations of this paper’s SWOT analysis, not of other reviews. (LLM, conf=0.73)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 47

  1. [3]Harnessing large language models in nursing care planning: opportunities, challenges, and ethical considerations Cureus Nashwan AJ Abujaber AA 0 15 2023 PMC10350541 37465807
  2. [5]Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study Turk J Emerg Med Sarbay İ Berikol GB Özturan İU 156 161 23 2023 37529789 PMC10389099
  3. [7]Enabling the adoption of machine learning in clinical decision support: A total interpretive structural modeling approach Inform Med Unlocked Abujaber AA Nashwan AJ Fadlalla A 101090 Berlin, Heidelberg: Springer Berlin Heidelberg 33 2022
  4. [9]A new curriculum for a for a NEW ERA of nursing education Nurs Educ Perspect Giddens J Brady D Brown P Wright M Smith D Harris J 200 204 29 2008 https://journals.lww.com/neponline/abstract/2008/07000/a_new_curriculum_for_a_for_a_new_era_of_nursing.10.aspx 18770947
  5. [12]From gatekeeping to engagement: a multicontextual, mixed method study of student academic engagement in introductory STEM courses Res High Educ Gasiewski JA Eagan MK Garcia GA Hurtado S Chang MJ 229 261 53 2012 23503751 PMC3596160
  6. [14]Using ChatGPT to teach enhanced clinical judgment in nursing education Nurse Educ Seney V Desroches ML Schuler MS 124 48 2023 36857593
  7. [15]Revolutionizing nursing education through Ai integration: A reflection on the disruptive impact of ChatGPT Nurse Educ Today Castonguay A Farthing P Davies S Vogelsang L Kleib M Risling T Green N 105916 129 2023 37515957
  8. [16]Experiences and perceptions of the theory-practice gap in nursing in a resource-constrained setting: A qualitative description study Nurs Open Salifu DA Gross J Salifu MA Ninnoni JP 72 83 6 2019 30534396 PMC6279732
  9. [17]ChatGPT and nursing education: challenges and opportunities Al-Rafidain Journal of Medical Sciences Alkhaqani AL 50 51 4 2023
  10. [20]The ChatGPT storm and what faculty can do Nurse Educ Sun GH Hoelscher SH 119 124 48 2023 37043716
  11. [21]ChatGPT for healthcare services: an emerging stage for an innovative perspective Ben Tran Ben Stan Eva Javaid M Haleem A Singh RP 100105 3 2023 https://www.sciencedirect.com/science/article/pii/S2772485923000224?via%3Dihub
  12. [22]Is ChatGPT leading generative AI? what is beyond expectations? Aca Pla Jr Eng Sm Sys Aydın Ö Karaarslan E 118 134 11 2023 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4341500
  13. [27]The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers JMIR Med Educ Eysenbach G 0 9 2023 PMC10028514 36863937
  14. [28]Harnessing the power of large language models (LLMs) for electronic health records (EHRs) optimization Cureus Nashwan AJ AbuJaber AA 0 15 2023 PMC10461074 37644945
  15. [31]A conversation with ChatGPT on the role of computational systems biology in stem cell research Stem Cell Reports Cahan P Treutlein B 1 2 18 2023 36630899 PMC9860153
  16. [34]Use of ChatGPT in academic publishing: a rare case of seronegative systemic lupus erythematosus in a patient with HIV infection Cureus Manohar N Prasad SS 0 15 2023 PMC9988440 36895547
  17. [37]Consistency analysis of ChatGPT arXiv Jang M Lukasiewicz T 23 2023
  18. [39]What ChatGPT and generative AI mean for science Nature Stokel-Walker C Van Noorden R 214 216 614 2023 36747115
  19. [40]ChatGPT is shaping the future of medical writing but still requires human judgment Radiology Kitamura FC 0 307 2023 36728749
  20. [41]A step-by-step researcher’s guide to the use of an AI-based transformer in epidemiology: an exploratory analysis of ChatGPT using the STROBE checklist for observational studies medRxiv Sanmarchi F Bucci A Golinelli D 2023 PMC10215032 37361298
  21. [42]Artificial intelligence discusses the role of artificial intelligence in translational medicine: a JACC: basic to translational science interview with ChatGPT JACC Basic Transl Sci Mann DL 221 223 8 2023 36908674 PMC9998448
  22. [43]ChatGPT in drug discovery Chemrxiv Sharma G Thakur A 2023
  23. [46]This new conversational AI model can be your friend, philosopher, and guide... and even your worst enemy Patterns (N Y) Chatterjee J Dethlefs N 100676 4 2023 36699746 PMC9868650
  24. [50]ChatGPT: the next frontier in academic writing for cardiologists or a pandora's box of ethical dilemmas Eur Heart J Open Marchandot B Matsushita K Carmona A Trimaille A Morel O 0 3 2023 PMC10006694 36915398
  25. [51]ChatGPT and the future of medical writing Radiology Biswas S 0 307 2023 36728748
  26. [52]Generating scholarly content with ChatGPT: ethical challenges for medical publishing Lancet Digit Health Liebrenz M Schleifer R Buadze A Bhugra D Smith A 105 106 5 2023 36754725
  27. [54]Scientific novelty beyond the experiment Microb Biotechnol Hallsworth JE Udaondo Z Pedrós-Alió C 1131 1173 16 2023 36786388 PMC10221578
  28. [56]Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios J Med Syst Cascella M Montomoli J Bellini V Bignami E 33 47 2023 36869927 PMC9985086
  29. [59]ChatGPT: the future of discharge summaries? Lancet Digit Health Patel SB Lam K 107 108 5 2023 36754724
  30. [60]Using ChatGPT to write patient clinic letters Lancet Digit Health Ali SR Dobbs TD Hutchings HA Whitaker IS 179 181 5 2023 36894409
  31. [61]A categorical archive of ChatGPT failures arXiv Borji A 23 2023
  32. [62]AI-based modeling: techniques, applications, and research issues towards automation, intelligent and smart systems SN Comput Sci Sarker IH 158 3 2022 35194580 PMC8830986
  33. [64]Libyan nurses and their training needs: an overview and analysis of TNA as a tool to enhance nurse learning and effectiveness GSTF Journal on Business Review Mahfod J 3 2014 http://dl6.globalstf.org/index.php/gbr/article/view/244
  34. [65]Training needs analysis of Korean nurses' neurological assessment competency Nurs Health Sci Bae KS Roh YS 99 107 22 2020 31609541
  35. [68]Using chatbots as AI conversational partners in language learning Applied Sciences Belda-Medina J Calvo-Ferrer JR 8427 12 2022 PMC9136798 35668897
  36. [70]A center-based model for self-directed learning in sustainability: engaging campus and community as a living lab Self-Directed Learning and the Academic Evolution From Pedagogy to Andragogy Manning L Jones J Buehlman V Deal J Showalter L 118 IGI Global https://www.igi-global.com/chapter/a-center-based-model-for-self-directed-learning-in-sustainability/294366
  37. [71]Adolescents' enjoyment and effort in class: Influenced by self-directed learning intervals J Sch Psychol Schweder S Raufelder D 72 89 95 2022 36371126
  38. [72]Effectiveness of a self-directed learning program using blended coaching among nursing students in clinical practice: a quasi-experimental research design BMC Med Educ Noh GO Kim DH 225 19 2019 31234843 PMC6591891
  39. [73]Self-directed learning in nurse education: a review of the literature J Adv Nurs O'Shea E 62 70 43 2003 12801397
  40. [74]Intelligent learning environments: design, usage and analytics for future schools Shaping Future Schools With Digital Technology: An International Handbook Mavrikis M Holmes W 57 73 Springer 2019
  41. [80]A virtual counseling application using artificial intelligence for communication skills training in nursing education: development study J Med Internet Res Shorey S Ang E Yap J Ng ED Lau ST Chui CK 0 21 2019 PMC6913997 31663857
  42. [81]Horizon report: knowledge obsolescence, artificial intelligence, and rethinking the educator role Nurs Educ Perspect Skiba DJ 165 167 38 2017 36785480
  43. [83]Virtual nursing avatars: nurse roles and evolving concepts of care Online J Issues Nurs Abbott M Shaw P 7 21 2016 27857172
  44. [85]Collaborative learning in higher nursing education: a systematic review J Prof Nurs Zhang J Cui Q 378 388 34 2018 30243695
  45. [86]The effect of cooperative learning on critical thinking of nursing students in clinical practicum: A quasi-experimental study J Prof Nurs Zhang J Chen B 177 183 37 2021 33674089
  46. [87]Artificial intelligent robots for precision education Edu Tech Soc Chen X Cheng G Zou D Zhong B Xie H 171 186 26 2023 https://www.jstor.org/stable/48707975
  47. [91]SWOT analysis: a theoretical review J Int Soc Res Gurl E 2017 https://www.researchgate.net/publication/319367788_SWOT_ANALYSIS_A_THEORETICAL_REVIEW

Cited by

Sorted by citations

Authors
Abujaber, A. A., Abd-alrazaq, A., Al-Qudimat, A. R., & Nashwan, A. J.
Affiliations
Weill Cornell Medical College in Qatar (QA)
Corresponding author
Nashwan, Abdulqadir J
ORCID
Venue
Cureus
Publisher
Springer Science and Business Media LLC
Published
2023-11-11
Volume
15
Issue
11
Pages
e48643-e48643
ISSN
2168-8184 (ISSN-L)
DOI
10.7759/cureus.48643
PMC
PMC10711690
OpenAlex
W4388593829
Cited by
91
References
29
Citations by year
2026: 22 · 2025: 46 · 2024: 23
FWCI
1.07
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Cureus (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
ChatGPT, large language models, nursing education, artificial intelligence in education, virtual clinical simulation, personalized learning, cooperative learning, educational technology integration, responsible AI use

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review