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Beyond the Prompt: Leveraging Generative AI and Tanner's Model to Decode Clinical Logic in Undergraduate Nursing.

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Title: Beyond the Prompt: Leveraging Generative AI and Tanner's Model to Decode Clinical Logic in Undergraduate Nursing.
Authors: Zeigler K; Author Affiliations: School of Nursing, Georgia Southern University, Savannah, Georgia (Dr Zeigler); and School of Nursing, Georgia Southern University, Statesboro, Georgia (Dr Bester)., Bester ME
Source: Nurse educator [Nurse Educ] 2026 Sep-Oct 01; Vol. 51 (5), pp. 276-278. Date of Electronic Publication: 2026 Jun 23.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 7701902 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1538-9855 (Electronic) Linking ISSN: 03633624 NLM ISO Abbreviation: Nurse Educ Subsets: MEDLINE
Imprint Name(s): Publication: Philadelphia, PA : Lippincott Williams & Wilkins
Original Publication: [Wakefield, Mass., Nursing Digest, Inc.]
MeSH Terms: Education, Nursing, Baccalaureate*/organization & administration , Students, Nursing*/psychology , Students, Nursing*/statistics & numerical data , Logic* , Thinking* , Clinical Reasoning*, Humans ; Generative Artificial Intelligence ; Nursing Education Research ; Nursing Evaluation Research ; Algorithms
Abstract: Background: Transitioning from theory to clinical practice is a persistent hurdle in undergraduate nursing programs, largely because traditional teaching methods lack the frequent, iterative practice needed to close the practice-readiness gap.
Purpose: This article describes an innovative pedagogical approach that integrates Generative Artificial Intelligence (GenAI) with Tanner's Clinical Judgment Model to develop nonlinear, algorithmic thinking patterns.
Methods: Using an "If This, Then That" logic framework, students use GenAI as a "thinking partner" to build clinical logic. The process follows Tanner's 4 phases: Noticing, Interpreting, Responding, and Reflecting. Students explore consequential pathways and refine their clinical hypotheses in a low-stakes environment.
Results: The AI-augmented iterative algorithm moves students away from rote memorization and toward spatial-logical mapping, promoting metacognition, and transitioning students from linear thinking to complex pattern recognition.
Conclusions: When used as a scaffolded, iterative tool, GenAI enhances clinical synthesis and prepares students for the "chaos of the floor."
(Copyright © 2026 Wolters Kluwer Health, LLC. All rights reserved.)
Competing Interests: The authors declare no conflicts of interest.
References: Bradley CS, Thomas J, Yip W, Aebersold M. In pursuit of the practice ready nurse: insights from a national survey. J Nurs Adm. 2025;55(4):222-229. doi:10.1097/NNA.0000000000001565. (PMID: 10.1097/NNA.0000000000001565)
Stratton-Maher D, Tulleners T, Elliott J, et al. Re-imagining nursing education: mapping student learning, transition, and workforce readiness—a scoping review. Int J Nurs Stud. 2026;178:105368. doi:10.1016/j.ijnurstu.2026.105368. (PMID: 10.1016/j.ijnurstu.2026.105368)
Brown J, Meza C, Sandiford D. Mitigating cognitive overload in nursing education: an integrative review. Nurse Educ. 2026. doi:10.1097/NNE.0000000000002126. (PMID: 10.1097/NNE.0000000000002126)
Tanner CA. Thinking like a nurse: a research-based model of clinical judgment in nursing. J Nurs Educ. 2006;45(6):204-211. doi:10.3928/01484834-20060601-04. (PMID: 10.3928/01484834-20060601-04)
National Council of State Boards of Nursing. Clinical Judgment Measurement Model. Accessed April 19, 2026.
Hassani P, Abdi A, Jalali R. State of science, "intuition in nursing practice": a systematic review study. J Clin Diagn Res. 2016;10(2):JE07-JE11. doi:10.7860/JCDR/2016/17385.7260. (PMID: 10.7860/JCDR/2016/17385.7260)
Schroeder E, Johnson J. From prompting to practice: empowering faculty with AI and prompting tools. Nurse Educ Today. 2026;163:107096. doi:10.1016/j.nedt.2026.107096. (PMID: 10.1016/j.nedt.2026.107096)
Boman M, Jhun P, Schaekermann M. Scaffolding for success: blending learning with and about generative AI in medical education. Med Teach. 2025;47(12):1911-1917. doi:10.1080/0142159X.2025.2571041. (PMID: 10.1080/0142159X.2025.2571041)
Mallari M, Tariman J. Ethical frameworks for decision-making in nursing practice and research: an integrative literature review. J Nurs Pract Appl Rev Res. 2017;7(1):7-15. doi:10.13178/jnparr.2017.0701.0707. (PMID: 10.13178/jnparr.2017.0701.0707)
Logan AM. Artificial Intelligence in Nursing Education: Transforming Clinical Judgment Training with Smart Case Studies. Poster presented at: National Nursing Education Summit; May 2025.
Ur B, McManus E, Pak Yong Ho M, Littman ML. Practical trigger-action programming in the smart home. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014:803-812. doi:10.1145/2556288.2557420.
Forsberg E, Ziegert K, Hult H, Fors U. Clinical reasoning in nursing, a think-aloud study using virtual patients—a base for an innovative assessment. Nurse Educ Today. 2014;34(4):538-542. doi:10.1016/j.nedt.2013.07.010. (PMID: 10.1016/j.nedt.2013.07.010)
Levett-Jones T, Hoffman K, Dempsey J, et al. The 'five rights' of clinical reasoning: an educational model to enhance nursing students' ability to identify and manage clinically 'at risk' patients. Nurse Educ Today. 2010;30(6):515-520. doi:10.1016/j.nedt.2009.10.020. (PMID: 10.1016/j.nedt.2009.10.020)
Pérez-Perdomo A, Zabalegui A. Teaching strategies for developing clinical reasoning skills in nursing students: a systematic review of randomised controlled trials. Healthcare (Basel). 2023;12(1):90. doi:10.3390/healthcare12010090. (PMID: 10.3390/healthcare12010090)
Jelloul H, Arhouh S, Rmili M, Nafidi Y. Artificial intelligence–enabled educational tools and clinical reasoning in undergraduate nursing education: a systematic review. J Pioneer Med Sci. 2026;15(02):192-203. doi:10.47310/jpms2026150226. (PMID: 10.47310/jpms2026150226)
Sajja R, Sermet Y, Chi N, Demir I. Evaluating artificial intelligence-assisted nursing education: student perceptions, ethical concerns, and pedagogical implications. Teach Learn Nurs. 2026;21(2):146-154. doi:10.1016/j.teln.2025.09.010. (PMID: 10.1016/j.teln.2025.09.010)
National League for Nursing. Vision Statement: Integration of AI in Nursing Education. 2025.
Contributed Indexing: Keywords: If This; Then That (IFTTT) Logic; clinical reasoning; critical thinking; practice readiness; teaching innovation
Entry Date(s): Date Created: 20260622 Date Completed: 20260825 Latest Revision: 20260825
Update Code: 20260826
DOI: 10.1097/NNE.0000000000002230
PMID: 42330069
Database: MEDLINE
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  Data: Beyond the Prompt: Leveraging Generative AI and Tanner's Model to Decode Clinical Logic in Undergraduate Nursing.
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  Data: Background: Transitioning from theory to clinical practice is a persistent hurdle in undergraduate nursing programs, largely because traditional teaching methods lack the frequent, iterative practice needed to close the practice-readiness gap.<br />Purpose: This article describes an innovative pedagogical approach that integrates Generative Artificial Intelligence (GenAI) with Tanner's Clinical Judgment Model to develop nonlinear, algorithmic thinking patterns.<br />Methods: Using an "If This, Then That" logic framework, students use GenAI as a "thinking partner" to build clinical logic. The process follows Tanner's 4 phases: Noticing, Interpreting, Responding, and Reflecting. Students explore consequential pathways and refine their clinical hypotheses in a low-stakes environment.<br />Results: The AI-augmented iterative algorithm moves students away from rote memorization and toward spatial-logical mapping, promoting metacognition, and transitioning students from linear thinking to complex pattern recognition.<br />Conclusions: When used as a scaffolded, iterative tool, GenAI enhances clinical synthesis and prepares students for the "chaos of the floor."<br /> (Copyright © 2026 Wolters Kluwer Health, LLC. All rights reserved.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: The authors declare no conflicts of interest.
– Name: Ref
  Label: References
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  Data: Bradley CS, Thomas J, Yip W, Aebersold M. In pursuit of the practice ready nurse: insights from a national survey. J Nurs Adm. 2025;55(4):222-229. doi:10.1097/NNA.0000000000001565. (PMID: <searchLink fieldCode="PM" term="%2210%2E1097%2FNNA%2E0000000000001565%22">10.1097/NNA.0000000000001565)</searchLink><br />Stratton-Maher D, Tulleners T, Elliott J, et al. Re-imagining nursing education: mapping student learning, transition, and workforce readiness—a scoping review. Int J Nurs Stud. 2026;178:105368. doi:10.1016/j.ijnurstu.2026.105368. (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Eijnurstu%2E2026%2E105368%22">10.1016/j.ijnurstu.2026.105368)</searchLink><br />Brown J, Meza C, Sandiford D. Mitigating cognitive overload in nursing education: an integrative review. Nurse Educ. 2026. doi:10.1097/NNE.0000000000002126. (PMID: <searchLink fieldCode="PM" term="%2210%2E1097%2FNNE%2E0000000000002126%22">10.1097/NNE.0000000000002126)</searchLink><br />Tanner CA. Thinking like a nurse: a research-based model of clinical judgment in nursing. J Nurs Educ. 2006;45(6):204-211. doi:10.3928/01484834-20060601-04. (PMID: <searchLink fieldCode="PM" term="%2210%2E3928%2F01484834-20060601-04%22">10.3928/01484834-20060601-04)</searchLink><br />National Council of State Boards of Nursing. Clinical Judgment Measurement Model. Accessed April 19, 2026.<br />Hassani P, Abdi A, Jalali R. State of science, "intuition in nursing practice": a systematic review study. J Clin Diagn Res. 2016;10(2):JE07-JE11. doi:10.7860/JCDR/2016/17385.7260. (PMID: <searchLink fieldCode="PM" term="%2210%2E7860%2FJCDR%2F2016%2F17385%2E7260%22">10.7860/JCDR/2016/17385.7260)</searchLink><br />Schroeder E, Johnson J. From prompting to practice: empowering faculty with AI and prompting tools. Nurse Educ Today. 2026;163:107096. doi:10.1016/j.nedt.2026.107096. (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Enedt%2E2026%2E107096%22">10.1016/j.nedt.2026.107096)</searchLink><br />Boman M, Jhun P, Schaekermann M. Scaffolding for success: blending learning with and about generative AI in medical education. Med Teach. 2025;47(12):1911-1917. doi:10.1080/0142159X.2025.2571041. (PMID: <searchLink fieldCode="PM" term="%2210%2E1080%2F0142159X%2E2025%2E2571041%22">10.1080/0142159X.2025.2571041)</searchLink><br />Mallari M, Tariman J. Ethical frameworks for decision-making in nursing practice and research: an integrative literature review. J Nurs Pract Appl Rev Res. 2017;7(1):7-15. doi:10.13178/jnparr.2017.0701.0707. (PMID: <searchLink fieldCode="PM" term="%2210%2E13178%2Fjnparr%2E2017%2E0701%2E0707%22">10.13178/jnparr.2017.0701.0707)</searchLink><br />Logan AM. Artificial Intelligence in Nursing Education: Transforming Clinical Judgment Training with Smart Case Studies. Poster presented at: National Nursing Education Summit; May 2025.<br />Ur B, McManus E, Pak Yong Ho M, Littman ML. Practical trigger-action programming in the smart home. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2014:803-812. doi:10.1145/2556288.2557420.<br />Forsberg E, Ziegert K, Hult H, Fors U. Clinical reasoning in nursing, a think-aloud study using virtual patients—a base for an innovative assessment. Nurse Educ Today. 2014;34(4):538-542. doi:10.1016/j.nedt.2013.07.010. (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Enedt%2E2013%2E07%2E010%22">10.1016/j.nedt.2013.07.010)</searchLink><br />Levett-Jones T, Hoffman K, Dempsey J, et al. The 'five rights' of clinical reasoning: an educational model to enhance nursing students' ability to identify and manage clinically 'at risk' patients. Nurse Educ Today. 2010;30(6):515-520. doi:10.1016/j.nedt.2009.10.020. (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Enedt%2E2009%2E10%2E020%22">10.1016/j.nedt.2009.10.020)</searchLink><br />Pérez-Perdomo A, Zabalegui A. Teaching strategies for developing clinical reasoning skills in nursing students: a systematic review of randomised controlled trials. Healthcare (Basel). 2023;12(1):90. doi:10.3390/healthcare12010090. (PMID: <searchLink fieldCode="PM" term="%2210%2E3390%2Fhealthcare12010090%22">10.3390/healthcare12010090)</searchLink><br />Jelloul H, Arhouh S, Rmili M, Nafidi Y. Artificial intelligence–enabled educational tools and clinical reasoning in undergraduate nursing education: a systematic review. J Pioneer Med Sci. 2026;15(02):192-203. doi:10.47310/jpms2026150226. (PMID: <searchLink fieldCode="PM" term="%2210%2E47310%2Fjpms2026150226%22">10.47310/jpms2026150226)</searchLink><br />Sajja R, Sermet Y, Chi N, Demir I. Evaluating artificial intelligence-assisted nursing education: student perceptions, ethical concerns, and pedagogical implications. Teach Learn Nurs. 2026;21(2):146-154. doi:10.1016/j.teln.2025.09.010. (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Eteln%2E2025%2E09%2E010%22">10.1016/j.teln.2025.09.010)</searchLink><br />National League for Nursing. Vision Statement: Integration of AI in Nursing Education. 2025.
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