The application of conversational artificial intelligence in nursing clinical reasoning education: A scoping review of roles, opportunities, and challenges

Abstract

Clinical reasoning is a core competency in nursing practice, yet its effective teaching faces challenges such as time constraints, limited resources, and insufficient individualized instruction. The rapid advancement of conversational artificial intelligence (CAI) in recent years offers new possibilities for innovating nursing education models. This scoping review aims to systematically map the current application of CAI in nursing clinical reasoning education, delineate its specific roles, analyze the educational opportunities it presents, and identify the main challenges and future directions. By synthesizing existing evidence, this review seeks to provide an evidence-based reference for educators, technology developers, and policymakers to promote the effective and responsible integration of this technology into nursing education. It also offers practical guidance for the application of conversational AI in clinical nursing teaching such as outpatient nursing training.

Keywords

Clinical reasoning / Conversational artificial intelligence / Educational technology / Nursing education / Simulated patient

References

[1] Lee J, Park CG, Kim SH, et al. Psychometric properties of a clinical reasoning assessment rubric for nursing education. BMC Nurs. 2021; 20(1): 177. PMid:34551737 https://doi.org/10.1186/s12912-021-00695-z

[2] Leal P, Poeira A, Mendes DA, et al. Teaching and Learning Clinical Reasoning in Nursing Education: A Student Training Course. Healthcare (Basel). 2024; 12(12). PMid:38921333 https://doi.org/10.3390/healthcare12121219

[3] Castonguay A, Farthing P, Davies S, et al. Revolutionizing nursing education through AI integration: A reflection on the disruptive impact of ChatGPT. Nurse Educ Today. 2023; 129: 105916. PMid:37515957 https://doi.org/10.1016/j.nedt.2023.105916

[4] Go J, Lee Y. The effectiveness of AI-based conversational agents in nursing education: A systematic review. Nurse Educ Pract. 2026; 91: 104728. PMid:41547257 https://doi.org/10.1016/j.nepr.2026.104728

[5] Han GI, Choi H, Son YJ. Advantages and challenges for utilization of generative artificial intelligence in clinical nursing practice: an integrative review. BMC Nurs. 2026.

[6] Lu K, Sun S, Liu W, et al. Mapping Key Nodes and Global Trends in AI and Large Language Models for Medical Education: A Bibliometric Study. Adv Med Educ Pract. 2025; 16: 1421-1438. PMid:40832621 https://doi.org/10.2147/AMEP.S538362

[7] Liaw SY, Rusli KDB, Tan JZ, et al. Artificial intelligence-enabled virtual reality simulation for clinical deterioration training: An effectiveness-implementation hybrid study. Nurse Educ Pract. 2025; 87: 104462. PMid:40627957 https://doi.org/10.1016/j.nepr.2025.104462

[8] Hall K, McKeever S, Ojabo M, et al. End-of-life education: An explorative study using artificial intelligence simulations in undergraduate nursing. Nurs Outlook. 2026; 74(1): 102652. PMid:41412086 https://doi.org/10.1016/j.outlook.2025.102652

[9] García-Torres D, Vicente Ripoll MA, Fernández Peris C, et al. Enhancing Clinical Reasoning with Virtual Patients: A Hybrid Systematic Review Combining Human Reviewers and ChatGPT. Healthcare (Basel). 2024; 12(22). PMid:39595439 https://doi.org/10.3390/healthcare12222241

[10] Brügge E, Ricchizzi S, Arenbeck M, et al. Large language models improve clinical decision making of medical students through patient simulation and structured feedback: a randomized controlled trial. BMC Med Educ. 2024; 24(1): 1391. PMid:39609823 https://doi.org/10.1186/s12909-024-06399-7

[11] Wang Z, Fan TT, Li ML, et al. Feasibility study of using GPT for history-taking training in medical education: a randomized clinical trial. BMC Med Educ. 2025; 25(1): 1030. PMid:40640776 https://doi.org/10.1186/s12909-025-07614-9

[12] Hamadouk RM, Albashair ED, Alshareif EM, et al. Community pharmacists’ history taking practice in responding to acute uncomplicated cystitis: A simulated patient study from Sudan. PLoS One. 2026; 21(2): e0343006. PMid:41701693 https://doi.org/10.1371/journal.pone.0343006

[13] Cohen S, Vempati A, Lei C, et al. Clinical Decision-Making Case: Thyroid Storm. J Educ Teach Emerg Med. 2025; 10(5): CE260-CE291.

[14] Stamer T, Steinhäuser J, Flägel K. Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review. J Med Internet Res. 2023; 25: e43311. PMid:37335593 https://doi.org/10.2196/43311

[15] Waechter J, Allen J, Lee CH, et al. Development and Pilot Testing of a Data-Rich Clinical Reasoning Training and Assessment Tool. Acad Med. 2022; 97(10): 1484-1488. PMid:35612911 https://doi.org/10.1097/ACM.0000000000004758

[16] Shen M, Shen Y, Shi Z. Exploring nursing students’ acceptance of RAG-enhanced GenAI through the AIDUA model: A qualitative study. Nurse Educ Pract. 2025; 89: 104612. PMid:41161072 https://doi.org/10.1016/j.nepr.2025.104612

[17] Gamby S, Bauer CF. Beyond “study skills”: a curriculum-embedded framework for metacognitive development in a college chemistry course. Int J STEM Educ. 2022; 9(1): 61. PMid:36187222 https://doi.org/10.1186/s40594-022-00376-6

[18] Soni M, Eidelman AS. Opportunities for narrative medicine in dental education and practice, a narrative review. J Dent Educ. 2022; 86(3): 328-333. PMid:34647322 https://doi.org/10.1002/jdd.12806

[19] Amedu C, Ohene-Botwe B. Harnessing the benefits of ChatGPT for radiography education: A discussion paper. Radiography (Lond). 2024; 30(1): 209-216. PMid:38035435 https://doi.org/10.1016/j.radi.2023.11.009

[20] Jacobs C, Hazra K, Singh A. Enhancing GP consultation skills training: educational evaluation of a conversational AI innovation for simulated consultation assessment preparation. Educ Prim Care. 2025; 36(6): 291-299. PMid:40906903 https://doi.org/10.1080/14739879.2025.2551207

[21] Fu Z. Integrating Reinforcement Learning with Dynamic Knowledge Tracing for personalized learning path optimization. Sci Rep. 2025; 15(1): 40202. PMid:41249280 https://doi.org/10.1038/s41598-025-23900-4

[22] Tan C, Han R, Ye R, et al. Adaptive Learning Recommendation Strategy Based on Deep Q-learning. Appl Psychol Meas. 2020; 44(4): 251-266. PMid:32536728 https://doi.org/10.1177/0146621619858674

[23] Triberti S, Di Fuccio R, Scuotto C, et al. “Better than my professor?” How to develop artificial intelligence tools for higher education. Front Artif Intell. 2024; 7: 1329605. PMid:38665370 https://doi.org/10.3389/frai.2024.1329605

[24] McFarlane A, Sadri S, Schwartz E, et al. Resident Physicians’ Perceptions of Artificial Intelligence and Implications for Medical Education: A Qualitative Study. MedEd Publish (2016). 2025; 15: 29. PMid:41625979 https://doi.org/10.12688/mep.20833.1

[25] Shukla M, Pandey D, Agarwal M, et al. Assessing the Capabilities of Artificial Intelligence (AI) Tools in Community Medicine: A Comparative Study of ChatGPT, Gemini, and Bing in Community-Based Clinico-Social Case Interpretation. Cureus. 2025; 17(9): e91917.

[26] An F, Yu J, Xi L. Relationship between perceived teacher support and learning engagement among adolescents: Mediation role of technology acceptance and learning motivation. Front Psychol. 2022; 13: 992464. PMid:36248506 https://doi.org/10.3389/fpsyg.2022.992464

[27] Forsyth A. Safe, Not Shiny: Sustaining Digital Health Innovation. Stud Health Technol Inform. 2026; 334: 24-28.

[28] Sahu PK, Kumar A, Williams-Persad A, et al. A scoping literature review of artificial intelligence integration in higher education for enhanced teaching, learning, and assessment. Panminerva Med. 2026.

[29] Tuncer M, Yalcinkaya T. Evaluation of ChatGPT-4, Gemini, Claude, and Copilot in Generating Nursing Diagnoses Based on NANDA-I Taxonomy II: A Comparative Cross-Sectional Study. Int Nurs Rev. 2025; 72(4): e70135. PMid:41311033 https://doi.org/10.1111/inr.70135

[30] Saatçi G, Ünsal A. Development and Psychometric Validation of the Attitude Scale for Nursing Specialization. J Nurs Meas. 2025.

[31] Choperena A, Pardavila-Belio MI, Errasti-Ibarrondo B, et al. Implementation and evaluation of a training programme to promote the development of professional competences in nursing: A pilot study. Nurse Educ Today. 2020; 87: 104360. PMid:32135456 https://doi.org/10.1016/j.nedt.2020.104360

[32] Li Z, Lu F, Abshire Saylor M, et al. Characteristics and Effectiveness of Nurse-Led Family Communication Interventions in the ICU: A Systematic Review and Meta-Analysis. Crit Care Med. 2026; 54(2): 301-311. PMid:41236180 https://doi.org/10.1097/CCM.0000000000006952

[33] Simoni J, Urtubia-Fernandez J, Mengual E, et al. Artificial intelligence in undergraduate medical education: an updated scoping review. BMC Med Educ. 2025; 25(1): 1609. PMid:41250109 https://doi.org/10.1186/s12909-025-08188-2

[34] Pan H, Gong MC, Lu JH, et al. Expert Consensus on Artificial Intelligence Proficiency for Medical Educators(2025 Edition). Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2026.

[35] El-Banna MM, Sajid MR, Rizvi MR, et al. AI literacy and competency in nursing education: preparing students and faculty members for an AI-enabled future-a systematic review and meta-analysis. Front Med (Lausanne). 2025; 12: 1681784. PMid:41384110 https://doi.org/10.3389/fmed.2025.1681784

[36] Sarkar S, Sharma S, Raheja S. Implementation of Blended Learning Approach for Improving Anatomy Lectures of Phase I MBBS Students - Learner Satisfaction Survey. Adv Med Educ Pract. 2021; 12: 413-420. PMid:33935527 https://doi.org/10.2147/AMEP.S301634

[37] Gin BC, LaForge K, Burk-Rafel J, et al. Macy Foundation Innovation Report Part II: From Hype to Reality: Innovators’ Visions for Navigating AI Integration Challenges in Medical Education. Acad Med. 2025; 100(9S Suppl 1): S22-S29. PMid:40479503 https://doi.org/10.1097/ACM.0000000000006117