Artificial Intelligence-Based Adaptive Simulation Integrated With a Workforce Decision-Support System to Improve Simulated Nursing Leadership Performance: A Randomized Controlled Study
Open accessAbstract
Background The growing integration of artificial intelligence (AI) in healthcare necessitates that nurse leaders develop advanced competencies in decision-making, communication, and data-informed clinical judgment. Traditional simulation-based education, while effective, often relies on fixed scenarios with limited adaptability and minimal real-time decision support. AI-enhanced simulation offers a potential solution by creating responsive, adaptive learning environments that support leadership development. Methods This randomized controlled, parallel-group, pretest-posttest trial included 130 final-year nursing students who were randomly allocated to an intervention group (n = 65), receiving AI-adaptive simulation integrated with an AI-based workforce decision-support system, or a control group (n = 65). Twenty-one clinical preceptors served as trained and blinded outcome assessors and were not included in the randomized sample. Leadership performance was assessed using the Creighton Simulation Evaluation Instrument (C-SEI). Secondary outcomes included team performance, general self-efficacy, and AI acceptance. Results At Week 10, the intervention group demonstrated greater improvement in leadership performance than the control group (between-group difference in change = 5.20, 95% CI 4.18-6.22, p < 0.001), with a large standardized effect (Cohen's d = 1.78). Greater improvements were also observed in team performance (between-group difference in change = 0.63, 95% CI 0.44-0.82, p < 0.001, d = 1.24) and general self-efficacy (between-group difference in change = 5.30, 95% CI 3.82-6.78, p < 0.001, d = 1.37). Intervention-group students reported favorable AI acceptance, including perceived usefulness and ease of use. Conclusions AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved leadership performance, team performance, and self-efficacy among final-year nursing students under controlled simulation conditions. Further research is needed to determine whether these effects are sustained and transfer to clinical practice.
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Written from the paper’s full text.
TL;DR
In a randomized controlled trial with 130 final-year nursing students, adding AI-adaptive simulation plus an AI workforce decision-support system improved simulated leadership performance, team performance, and self-efficacy versus conventional clinical placement. Effects were large, but outcomes were measured only in a high-fidelity simulation setting at Week 10.
Key findings
- Primary outcome: leadership performance on the C-SEI improved more in the intervention group (between-group difference in change = 5.20, 95% CI 4.18–6.22, p < 0.001, Cohen’s d = 1.78); intervention scores rose from 15.80 ± 3.00 at Week 1 to 21.70 ± 2.80 at Week 10, versus 15.70 ± 3.10 to 16.40 ± 3.20 in controls.
- Secondary outcomes also favored the intervention: team performance difference = 0.63 (95% CI 0.44–0.82, p < 0.001, d = 1.24) and general self-efficacy difference = 5.30 (95% CI 3.82–6.78, p < 0.001, d = 1.37).
- AI acceptance among intervention students was favorable: perceived usefulness 4.40 ± 0.60 and perceived ease of use 4.20 ± 0.70 on a five-point scale.
- In multiple regression, AI exposure (B = 3.12, β = 0.52, p < 0.001) and self-efficacy (B = 0.67, β = 0.33, p = 0.004) predicted Week 10 leadership performance; the model explained 46% of variance in the prose, with table R² = 0.48.
Methods
- Randomized controlled, parallel-group, pretest-posttest trial at the College of Nursing and affiliated clinical training facility, Taif University; 130 final-year nursing students were randomized 65 to intervention and 65 to control, with 21 clinical preceptors serving as trained, blinded outcome assessors.
- Intervention students completed two AI-adaptive high-fidelity ICU simulation sessions in Week 2 involving sepsis deterioration and staffing shortages, with an AI dashboard and ChatGPT GPT-4o role-play; controls continued conventional clinical placement without simulation or AI decision-support tools.
- Outcomes were C-SEI leadership performance, TeamSTEPPS team performance, GSES self-efficacy, and an adapted TAM AI acceptance questionnaire; analyses used SPSS v29, between-group change scores with 95% CIs and Cohen’s d, and multiple regression. Reliability: C-SEI α = 0.91, ICC = 0.89 (95% CI 0.84–0.93); TeamSTEPPS α = 0.88; AI acceptance α = 0.90; GSES α = 0.87.
Limitations
- The paper states: single institution/site; predominantly final-year nursing students; simulation cannot fully replicate clinical practice; follow-up limited to immediate post-intervention; no a priori sample-size calculation; C-SEI is not leadership-specific; participant blinding was not feasible.
- Evident from the text: Table 3 and Table 4 report inconsistent GSES values (e.g., control Week 1 27.80 ± 4.60 vs Table 4 pretest 21.30 ± 4.20; intervention Week 1 28.00 ± 4.50 vs Table 4 pretest 21.70 ± 4.50), and the regression reports F(3, 147) despite a randomized sample of 130.
- Evident from the text: the conclusion compares the intervention with “standard high-fidelity simulation,” but the control group is described as receiving conventional clinical placement without simulation or AI-based activities.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Claims the paper makes
33 of 50 cited sources checked · 16 supported · 1 flagged · accuracy not yet measured
- Premisecitation
AI can support knowledge development, clinical reasoning, learner confidence, and engagement when embedded as a pedagogical resource rather than introduced as a stand-alone technical component.
“Reviews published between 2021 and 2026 suggest that AI can support knowledge development, clinical reasoning, learner confidence, and engagement when embedded as a pedagogical resource rather than introduced as a stand-alone technical component [3,4].”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [3] Bozkurt SA, Aydoğan S, Dursun Ergezen F, Türkoğlu A. A systematic review and sequential explanatory synthesis: artificial intelligence in healthcare education, a case of nursing. Int Nurs Rev. 2025, 72:70018. 10.1111/inr.70018 source
The Conclusions and Recommendations and Abstract (Implications for nursing practice and nursing policy; page numbers unavailable) describe gains in knowledge, clinical decision-making, confidence, and engagement among nursing students. They recommend careful integration into education, but do not establish the claimed contrast between embedding AI as a pedagogical resource and introducing it as a stand-alone technical component. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
- Premisecitation
Leadership-focused simulation can strengthen delegation, communication, teamwork, and problem-solving skills required for safe care delivery.
“leadership-focused simulation can strengthen delegation, communication, teamwork, and problem-solving skills required for safe care delivery [5,6].”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [5] Görücü S, Türk G, Karaçam Z. The effect of simulation-based learning on nursing students' clinical decision-making skills: systematic review and meta-analysis. Nurse Educ Today. 2024, 140:106270. 10.1016/j.nedt.2024.106270 source
- cites [6] Labrague LJ. Use of simulation in teaching nursing leadership and management course. Sultan Qaboos Univ Med J. 2021, 21:344-53. source
- Premisecitation
Simulation can improve clinical decision-making.
“simulation can improve clinical decision-making”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [5] Görücü S, Türk G, Karaçam Z. The effect of simulation-based learning on nursing students' clinical decision-making skills: systematic review and meta-analysis. Nurse Educ Today. 2024, 140:106270. 10.1016/j.nedt.2024.106270 source
- cites [6] Labrague LJ. Use of simulation in teaching nursing leadership and management course. Sultan Qaboos Univ Med J. 2021, 21:344-53. source
- Premisecitation
Studies involving AI-supported virtual, narrative, and generative simulations have reported improvements in self-efficacy, communication knowledge, perceived competence, diagnostic reasoning, and clinical decision-making.
“Studies involving AI-supported virtual, narrative, and generative simulations have reported improvements in self-efficacy, communication knowledge, perceived competence, diagnostic reasoning, and clinical decision-making.”
Quote in the paperSubstantial claim3 of 5 sources checked- cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
The Results (Outcome; no page number in our copy) report gains in self-efficacy, communication knowledge, clinical competence, and clinical reasoning across AI-simulation studies. The review covers virtual and generative modalities, but the passages do not identify narrative simulation as a modality or establish a distinct improvement in clinical decision-making. It also cautions that many reported gains come from uncontrolled or self-reported studies. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [8] Díaz MJ. Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation. BMC Nurs. 2026, 25:105592. 10.1186/s12912-026-04359-8 source
The Results > Quantitative findings (page not specified) report higher self-efficacy, diagnostic accuracy, and NIC–NOC coherence for students using generative AI–enhanced narratives. The study used written narratives, not virtual simulations, and does not report improvements in communication knowledge or a separate measure of perceived competence or clinical decision-making. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [9] Fung TC, Chan SL, Lam CF, et al.. Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial. BMC Nurs. 2025, 24:934. 10.1186/s12912-025-03492-0 source
The Conclusion (page unavailable) reports improved perceived clinical competence, and the Results > Effect of the interventions on SET-M (page unavailable) reports greater confidence in assessment and communication and perceived opportunities to practise clinical decision-making. The paper describes diagnostic reasoning as a simulation activity, but does not report a measured improvement in it or in communication knowledge; it also does not study narrative simulations. (LLM, conf=0.86)
Experimental — accuracy not yet measured - cites [10] Liaw SY, Tan JZ, Lim S, et al.. Artificial intelligence in virtual reality simulation for interprofessional communication training: mixed method study. Nurse Educ Today. 2023, 122:105718. 10.1016/j.nedt.2023.105718 source
- cites [11] Salem MF, Sharaf AK, Younis AA. Impact of AI-powered adaptive ECG simulation training on nurses' knowledge, clinical decision-making and self-efficacy: a mixed-methods randomized controlled trial. Nurse Educ Pract. 2026, 95:104904. 10.1016/j.nepr.2026.104904 source
- Premisecitation
AI-supported simulation is most effective when it incorporates adaptive progression, real-time feedback, and clinically relevant learning experiences rather than static scenario exposure alone.
“The present study findings are consistent with recent literature indicating that AI-supported simulation is most effective when it incorporates adaptive progression, real-time feedback, and clinically relevant learning experiences rather than static scenario exposure alone [4,20].”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
- cites [20] Nugent L, Murray B, Moore Z, et al.. Teaching with intelligence; AI-enhanced pedagogies in nursing education: a scoping review. Nurse Educ Pract. 2026, 94:104878. 10.1016/j.nepr.2026.104878 source
- Premisecitation
These outcomes are particularly evident when learners receive real-time feedback and repeated practice within psychologically safe learning environments.
“These outcomes are particularly evident when learners receive real-time feedback and repeated practice within psychologically safe learning environments [4,8-11].”
Quote in the paperNot a substantial claim3 of 5 sources checked- cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
- cites [8] Díaz MJ. Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation. BMC Nurs. 2026, 25:105592. 10.1186/s12912-026-04359-8 source
The Results (Quantitative findings; page unavailable) report better diagnostic accuracy, NIC–NOC coherence, and self-efficacy in the AI-enhanced group. The Discussion (Mechanisms of learning; page unavailable) links repeated exposure and revision across narrative variations to learning, but the paper does not establish that these outcomes are particularly evident with real-time feedback or in psychologically safe learning environments. Its description of safe, controlled complexity is not a finding about psychological safety. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [9] Fung TC, Chan SL, Lam CF, et al.. Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial. BMC Nurs. 2025, 24:934. 10.1186/s12912-025-03492-0 source
The Discussion’s Clinical competence section (page unavailable) links GenAI’s real-time feedback to skill refinement and says personalized feedback likely contributed to improved performance. The paper does not establish that outcomes are particularly evident with repeated practice or in psychologically safe learning environments; its SET-M discussion describes a supportive environment but does not assess psychological safety. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [10] Liaw SY, Tan JZ, Lim S, et al.. Artificial intelligence in virtual reality simulation for interprofessional communication training: mixed method study. Nurse Educ Today. 2023, 122:105718. 10.1016/j.nedt.2023.105718 source
- cites [11] Salem MF, Sharaf AK, Younis AA. Impact of AI-powered adaptive ECG simulation training on nurses' knowledge, clinical decision-making and self-efficacy: a mixed-methods randomized controlled trial. Nurse Educ Pract. 2026, 95:104904. 10.1016/j.nepr.2026.104904 source
- Premisecitation
Rigorous experimental research integrating AI-adaptive simulation with decision-support tools in nursing leadership education remains limited.
“However, rigorous experimental research integrating AI-adaptive simulation with decision-support tools in nursing leadership education remains limited [4,9].”
Quote in the paperSubstantial claim2 of 2 sources checked- cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
The Discussion’s Principal Findings (page not provided) say that rigorous evidence for AI-driven simulation in nursing education is limited: only 3 of 19 studies were RCTs, and the evidence base was weighted toward uncontrolled or quasiexperimental designs. The review does not establish this specific gap for simulations integrated with decision-support tools in nursing leadership education. (LLM, conf=0.85)
Experimental — accuracy not yet measured - cites [9] Fung TC, Chan SL, Lam CF, et al.. Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial. BMC Nurs. 2025, 24:934. 10.1186/s12912-025-03492-0 source
The Introduction calls for further systematic evaluation of AI teaching approaches, and the Discussion (Implications of the findings) says larger studies are warranted. These passages support a general need for more research in nursing education, but do not establish that rigorous experiments combining AI-adaptive simulation with decision-support tools are limited specifically in nursing leadership education. (LLM, conf=0.82)
Experimental — accuracy not yet measured
- Findingown data
Self-efficacy was a significant predictor of Week 10 leadership performance (B = 0.67, SE = 0.22, β = 0.33, p = 0.004).
“| Self-efficacy | 0.67 | 0.22 | 0.33 | 3.05 | 0.004 |”
Quote in the paperNot a substantial claim - Findingown data
Intervention-group students reported favorable AI acceptance, including perceived usefulness and ease of use.
“Intervention-group students reported favorable AI acceptance, including perceived usefulness and ease of use.”
Quote in the paperNot a substantial claim - Findingown data
The intervention group showed a greater increase in general self-efficacy (GSES) from Week 1 to Week 10 than the control group, with a between-group difference in change of 5.30 (95% CI 3.82-6.78, p < 0.001, Cohen's d = 1.37).
“| General self-efficacy (GSES) | 27.80 ± 4.60 | 29.10 ± 4.40 | 28.00 ± 4.50 | 34.60 ± 3.90 | 5.30 | 3.82-6.78 | <0.001 | 1.37 |”
Quote in the paperSubstantial claim - Findingown data
AI exposure was a significant predictor of Week 10 leadership performance (B = 3.12, SE = 0.45, β = 0.52, p < 0.001).
“| AI exposure | 3.12 | 0.45 | 0.52 | 6.93 | <0.001 |”
Quote in the paperSubstantial claim- Rests on an assumption: The AI exposure variable used in the regression validly measures exposure to the AI intervention.
- Findingown data
The intervention group had shorter response times than the control group.
“The shorter response times and lower error rates observed in the intervention group align with the intended functions of AI-supported decision-support tools in healthcare.”
Quote in the paperSubstantial claim - Findingown data
Clinical experience was a significant predictor of Week 10 leadership performance (B = 0.84, SE = 0.38, β = 0.21, p = 0.03).
“| Clinical experience | 0.84 | 0.38 | 0.21 | 2.21 | 0.03 |”
Quote in the paperNot a substantial claim - Findingown data
The multiple regression model predicting Week 10 leadership performance had an R2 of 0.48.
“| R² | R² | R² | 0.48 | 0.48 | 0.48 |”
Quote in the paperNot a substantial claim - Findingown data
The intervention group had lower error rates than the control group.
“The shorter response times and lower error rates observed in the intervention group align with the intended functions of AI-supported decision-support tools in healthcare.”
Quote in the paperSubstantial claim - Findingown data
The intervention group showed a greater increase in team performance (TeamSTEPPS) from Week 1 to Week 10 than the control group, with a between-group difference in change of 0.63 (95% CI 0.44-0.82, p < 0.001, Cohen's d = 1.24).
“| Team performance (TeamSTEPPS) | 3.46 ± 0.51 | 3.58 ± 0.52 | 3.45 ± 0.50 | 4.20 ± 0.49 | 0.63 | 0.44-0.82 | <0.001 | 1.24 |”
Quote in the paperSubstantial claim - Findingown data
Intervention-group students reported favorable perceptions of the AI system, including perceived usefulness, perceived ease of use, and overall AI acceptance.
“Students reported favorable perceptions of the AI system, including perceived usefulness, perceived ease of use, and overall AI acceptance.”
Quote in the paperNot a substantial claim - Findingown data
At Week 10, the intervention group demonstrated greater improvement in team performance than the control group (between-group difference in change = 0.63, 95% CI 0.44-0.82, p < 0.001, d = 1.24).
“Greater improvements were also observed in team performance (between-group difference in change = 0.63, 95% CI 0.44-0.82, p < 0.001, d = 1.24)”
Quote in the paperSubstantial claim - Findingown data
The intervention group showed a greater increase in leadership performance (C-SEI) from Week 1 to Week 10 than the control group, with a between-group difference in change of 5.20 (95% CI 4.18-6.22, p < 0.001, Cohen's d = 1.78).
“| Leadership performance (C-SEI) | 15.70 ± 3.10 | 16.40 ± 3.20 | 15.80 ± 3.00 | 21.70 ± 2.80 | 5.20 | 4.18-6.22 | <0.001 | 1.78 |”
Quote in the paperSubstantial claim - Findingown data
At Week 10, the intervention group demonstrated greater improvement in general self-efficacy than the control group (between-group difference in change = 5.30, 95% CI 3.82-6.78, p < 0.001, d = 1.37).
“and general self-efficacy (between-group difference in change = 5.30, 95% CI 3.82-6.78, p < 0.001, d = 1.37).”
Quote in the paperSubstantial claim - Findingown data
The overall regression model explained 46% of the variance in Week 10 leadership performance.
“the overall model explained 46% of the variance in leadership performance.”
Quote in the paperNot a substantial claim - Study design and conductown data
The TeamSTEPPS Team Performance Observation Tool demonstrated acceptable to high internal consistency (Cronbach's α = 0.88).
“the TeamSTEPPS Team Performance Observation Tool, GSES, and AI Acceptance Scale demonstrated acceptable to high internal consistency.”
- Also stated in: § Results · plain
“| TeamSTEPPS Team Performance Observation Tool | 15 | 0.88 | Not analyzed | Not applicable |”
Quote in the paperNot a substantial claim - Also stated in: § Results · plain
- Interpretationown data
The present study findings support the use of AI-enhanced simulation as a structured approach to nursing leadership education under controlled learning conditions.
“The present study findings support the use of AI-enhanced simulation as a structured approach to nursing leadership education under controlled learning conditions.”
Quote in the paperSubstantial claim - Interpretationown data
AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved performance in a simulated nursing leadership setting.
“AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved performance in a simulated nursing leadership setting.”
Quote in the paperSubstantial claim - Interpretationown data
AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved self-efficacy among final-year nursing students under controlled simulation conditions.
“AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved leadership performance, team performance, and self-efficacy among final-year nursing students under controlled simulation conditions.”
Quote in the paperSubstantial claim - Study design and conductcitation
The C-SEI has established evidence of reliability and validity for simulation-based assessment.
“The C-SEI developed by Parsons et al. [14] was selected as the primary observational measure because it was developed to evaluate nursing student performance in simulated clinical environments and has established evidence of reliability and validity for simulation-based assessment.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] Parsons H, Hercinger T, Manz F. Improvement in scoring consistency for the Creighton Simulation Evaluation Instrument©. Clin Simul Nurs. 2012, 8:233-8. source
- Study design and conductnone given
All randomized students completed the baseline and Week 10 assessments.
“All randomized students completed the baseline and Week 10 assessments.”
Quote in the paperSubstantial claim - Study design and conductown data
The GSES showed a Cronbach's alpha coefficient of 0.87.
“Cronbach’s alpha coefficients were 0.91 for the C-SEI, 0.88 for the TeamSTEPPS Team Performance Observation Tool, 0.90 for the AI Acceptance Scale, and 0.87 for the GSES.”
Quote in the paperNot a substantial claim - Interpretationown data
AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved leadership performance among final-year nursing students under controlled simulation conditions.
“AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved leadership performance, team performance, and self-efficacy among final-year nursing students under controlled simulation conditions.”
Quote in the paperSubstantial claim - Study design and conductreasoning
The study was conducted at one institution and one affiliated clinical training site, which may limit the generalizability of the findings to other educational and clinical settings.
“First, the study was conducted at one institution and one affiliated clinical training site, which may limit the generalizability of the findings to other educational and clinical settings.”
Quote in the paperNot a substantial claim - Study design and conductown data
The TeamSTEPPS Team Performance Observation Tool showed a Cronbach's alpha coefficient of 0.88.
“Cronbach’s alpha coefficients were 0.91 for the C-SEI, 0.88 for the TeamSTEPPS Team Performance Observation Tool, 0.90 for the AI Acceptance Scale, and 0.87 for the GSES.”
Quote in the paperNot a substantial claim - Interpretationown data
The present findings add to the body of evidence by demonstrating the potential value of AI-responsive simulation in leadership scenarios involving competing priorities, patient deterioration, and resource constraints.
“The present findings add to this body of evidence by demonstrating the potential value of AI-responsive simulation in leadership scenarios involving competing priorities, patient deterioration, and resource constraints.”
Quote in the paperSubstantial claim - Study design and conductown data
The final sample provided statistical sensitivity to detect a large between-group difference in leadership performance at a two-sided significance level of α = 0.05.
“The final sample provided statistical sensitivity to detect a large between-group difference in leadership performance at a two-sided significance level of α = 0.05.”
Quote in the paperNot a substantial claim - Interpretationown data
AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved team performance among final-year nursing students under controlled simulation conditions.
“AI-adaptive simulation integrated with an AI-based workforce decision-support system was associated with improved leadership performance, team performance, and self-efficacy among final-year nursing students under controlled simulation conditions.”
Quote in the paperSubstantial claim - Premisecitation
The transfer of simulation learning to clinical practice is not always automatic.
“the transfer of simulation learning to clinical practice is not always automatic”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Høegh-Larsen AM, Gonzalez MT, Reierson IÅ, Husebø SI, Ravik M. Changes in nursing students' self-reported professional competence in simulation-based education and clinical placement: a longitudinal study. Nurse Educ Today. 2022, 119:105592. 10.1016/j.nedt.2022.105592 source
- Study design and conductown data
The C-SEI showed a Cronbach's alpha coefficient of 0.91.
“Cronbach’s alpha coefficients were 0.91 for the C-SEI, 0.88 for the TeamSTEPPS Team Performance Observation Tool, 0.90 for the AI Acceptance Scale, and 0.87 for the GSES.”
Quote in the paperNot a substantial claim - Study design and conductnone given
A formal a priori sample-size calculation was not performed before recruitment.
“A formal a priori sample-size calculation was not performed before recruitment.”
- Also stated in: § Discussion · plain
“Fifth, a formal a priori sample-size calculation was not performed before recruitment.”
Quote in the paperNot a substantial claim - Also stated in: § Discussion · plain
- Study design and conductreasoning
Participant blinding was not feasible because of the nature of the intervention.
“Finally, participant blinding was not feasible because of the nature of the intervention.”
Quote in the paperNot a substantial claim - Interpretationown data
Integrating adaptive simulation with AI-based decision support may provide a structured learning approach for developing nursing leadership competencies under controlled simulation conditions.
“These findings suggest that integrating adaptive simulation with AI-based decision support may provide a structured learning approach for developing nursing leadership competencies under controlled simulation conditions.”
Quote in the paperSubstantial claim - Study design and conductown data
At baseline, the groups had similar scores across leadership performance, team performance, and self-efficacy.
“At baseline, the groups had similar scores across the three measures.”
Quote in the paperSubstantial claim - Study design and conductown data
Exploratory factor analysis identified components representing the underlying structure of the AI Acceptance Scale, which accounted for a substantial proportion of the total variance.
“The exploratory factor analysis identified components representing the underlying structure of the AI Acceptance Scale, which accounted for a substantial proportion of the total variance.”
Quote in the paperNot a substantial claim - Interpretationreasoning
The observed improvement should be interpreted as improvement in leadership-related performance under controlled simulation conditions rather than evidence that the intervention improved leadership performance in routine clinical practice.
“Accordingly, the observed improvement should be interpreted as improvement in leadership-related performance under controlled simulation conditions rather than evidence that the intervention improved leadership performance in routine clinical practice.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 32
- May combine two claims. 21
- The quote only partly states this claim. 4
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe study was reported in accordance with the CONSORT 2025 statement for randomized trials.
“The study was reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) 2025 statement for randomized trials [13].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [13] Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ. 2010, 340:c332. 10.1136/bmj.c332 source
The Introduction identifies the guideline presented in this paper as CONSORT 2010, and the “Intent of CONSORT 2010” section explicitly calls this paper the CONSORT 2010 Statement. It is therefore not the CONSORT 2025 statement cited in the claim. This source also cannot establish whether the citing study followed that later statement. (LLM, conf=0.92)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationRecent nursing studies of adaptive ECG simulation and AI-enhanced diagnostic narratives reported that intervention participants demonstrated better decision-making, diagnostic accuracy, and self-efficacy than control participants.
“Similar findings have been reported in recent nursing studies of adaptive ECG simulation and AI-enhanced diagnostic narratives, in which intervention participants demonstrated better decision-making, diagnostic accuracy, and self-efficacy than control participants [8,11].”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [8] Díaz MJ. Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation. BMC Nurs. 2026, 25:105592. 10.1186/s12912-026-04359-8 source
- cites [11] Salem MF, Sharaf AK, Younis AA. Impact of AI-powered adaptive ECG simulation training on nurses' knowledge, clinical decision-making and self-efficacy: a mixed-methods randomized controlled trial. Nurse Educ Pract. 2026, 95:104904. 10.1016/j.nepr.2026.104904 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNursing learners generally report more favorable responses when AI tools are clearly relevant to clinical practice and introduced with appropriate guidance and support.
“Nursing learners generally report more favorable responses when AI tools are clearly relevant to clinical practice and introduced with appropriate guidance and support [2,10,23].”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites [2] El Arab RA, Alshakihs AH, Alabdulwahab SH, et al.. Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Front Digit Health. 2025, 7:6005. 10.3389/fdgth.2025.1666005 source
- cites [10] Liaw SY, Tan JZ, Lim S, et al.. Artificial intelligence in virtual reality simulation for interprofessional communication training: mixed method study. Nurse Educ Today. 2023, 122:105718. 10.1016/j.nedt.2023.105718 source
- cites [23] Jallad ST, Alsaqer K, Albadareen BI, Al-Maghaireh D. Artificial intelligence tools utilized in nursing education: incidence and associated factors. Nurse Educ Today. 2024, 142:106355. 10.1016/j.nedt.2024.106355 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationQualitative evidence suggests that realistic leadership scenarios can help nursing students clarify their leadership approaches, strengthen confidence in their decisions, and prepare for workplace complexity.
“Qualitative evidence further suggests that realistic leadership scenarios can help nursing students clarify their leadership approaches, strengthen confidence in their decisions, and prepare for workplace complexity [6,21,22].”
Quote in the paperNot a substantial claim2 of 3 sources checked- cites [6] Labrague LJ. Use of simulation in teaching nursing leadership and management course. Sultan Qaboos Univ Med J. 2021, 21:344-53. source
The Results section (page unavailable) reports that students in a descriptive simulation study expressed greater confidence in ward decisions, problem-solving and critical thinking. The Discussion and implications sections describe simulation’s potential to develop skills for future nursing roles, but the review does not establish that qualitative evidence shows students clarifying their own leadership approaches or becoming prepared for workplace complexity; it says the included studies were primarily quantitative. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [21] Cole L, Boss L, Fowler D, Cheatham L. Using leadership simulation scenarios with graduate nursing learners to support frontline nurse leader competency development. Nurse Lead. 2020, 19:405-10. source
- cites [22] Eid HR, Raaen AH, Grevskott T, Bjørgum M, Myrvold LM, Øvereng IA, Lysfjord EM. Nursing students' experiences of in situ simulation training in leadership practice: a qualitative explorative study. J Healthc Leadersh. 2026, 18:604428. 10.2147/JHL.S604428 source
Classified as probably not a substantial claim. May combine two claims.
PremisecitationAI-supported decision-support tools are designed to assist clinicians in recognizing clinical deterioration, processing complex information, prioritizing care actions, and reducing cognitive workload during time-sensitive situations.
“Such systems are designed to assist clinicians in recognizing clinical deterioration, processing complex information, prioritizing care actions, and reducing cognitive workload during time-sensitive situations [1].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [1] Atalla AD, El-Gawad Mousa MA, Hashish EA, Elseesy NA, Abd El Kader Mohamed AI, Sobhi Mohamed SM. Embracing artificial intelligence in nursing: exploring the relationship between artificial intelligence-related attitudes, creative self-efficacy, and clinical reasoning competency among nurses. BMC Nurs. 2025, 24:661. 10.1186/s12912-025-03306-3 source
The Literature review (AI and AI-related attitudes; no page provided) says AI-based decision-support systems analyze patient data in real time, offer recommendations to prevent deterioration, and help nurses make timely decisions. The Theoretical underpinning section says AI can analyze complex data and reduce nurses’ cognitive load. The paper discusses selecting appropriate interventions, but does not specifically say these tools prioritize care actions; its discussion is chiefly about nurses rather than clinicians generally. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationConcerns regarding realism, bias, and long-term transfer to practice have been reported.
“Concerns regarding realism, bias, and long-term transfer to practice have also been reported [4,8,9].”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
- cites [8] Díaz MJ. Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation. BMC Nurs. 2026, 25:105592. 10.1186/s12912-026-04359-8 source
- cites [9] Fung TC, Chan SL, Lam CF, et al.. Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial. BMC Nurs. 2025, 24:934. 10.1186/s12912-025-03492-0 source
The Introduction (page unavailable) notes the potential for AI to perpetuate bias. The Discussion’s Strengths and limitations and Future directions sections (pages unavailable) question whether perceived competence reflects actual clinical ability and call for longitudinal assessment of transfer to real-world practice. The paper describes its simulations as realistic rather than reporting a concern about their realism, so that part of the claim is unsupported. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAI adoption is influenced by perceived usefulness, self-efficacy, infrastructure, and leadership support.
“AI adoption is influenced by perceived usefulness, self-efficacy, infrastructure, and leadership support [2,3].”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [2] El Arab RA, Alshakihs AH, Alabdulwahab SH, et al.. Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Front Digit Health. 2025, 7:6005. 10.3389/fdgth.2025.1666005 source
- cites [3] Bozkurt SA, Aydoğan S, Dursun Ergezen F, Türkoğlu A. A systematic review and sequential explanatory synthesis: artificial intelligence in healthcare education, a case of nursing. Int Nurs Rev. 2025, 72:70018. 10.1111/inr.70018 source
The Results (Results from Quantitative Studies; page unavailable) report that nursing students’ intention to use AI correlates with performance expectancy, facilitating conditions, and self-efficacy. These partly correspond to usefulness and infrastructure, but the paper does not identify leadership support as a factor, and the reported outcome is intention to use AI rather than adoption itself. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFindings vary across outcomes, with some studies reporting stronger effects for structured cognitive tasks than for complex interpersonal or psychomotor performance.
“However, findings vary across outcomes, with some studies reporting stronger effects for structured cognitive tasks than for complex interpersonal or psychomotor performance.”
Quote in the paperNot a substantial claim3 of 3 sources checked- cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
- cites [8] Díaz MJ. Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation. BMC Nurs. 2026, 25:105592. 10.1186/s12912-026-04359-8 source
The Results > Quantitative findings (page unavailable) report large improvements in diagnostic accuracy and NIC–NOC coherence, supporting an effect on structured reasoning tasks. The paper does not measure interpersonal or psychomotor performance or compare effect sizes across those types of outcomes, so it does not support the claimed pattern of stronger effects. (LLM, conf=0.79)
Experimental — accuracy not yet measured - cites [9] Fung TC, Chan SL, Lam CF, et al.. Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial. BMC Nurs. 2025, 24:934. 10.1186/s12912-025-03492-0 source
The Discussion’s Cultural awareness section reports improvements in cognitive awareness but no significant change in the Behavior or Comfort with Interactions subscale, supporting variation across cognitive and interpersonal outcomes (page unavailable). The study measured self-perceived competency, not psychomotor performance, so it does not support that part of the comparison. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNursing students and practicing nurses often report moderate levels of AI readiness.
“nursing students and practicing nurses often report moderate levels of AI readiness”
Quote in the paperNot a substantial claim2 of 2 sources checked- cites [2] El Arab RA, Alshakihs AH, Alabdulwahab SH, et al.. Artificial intelligence in nursing: a systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Front Digit Health. 2025, 7:6005. 10.3389/fdgth.2025.1666005 source
- cites [3] Bozkurt SA, Aydoğan S, Dursun Ergezen F, Türkoğlu A. A systematic review and sequential explanatory synthesis: artificial intelligence in healthcare education, a case of nursing. Int Nurs Rev. 2025, 72:70018. 10.1111/inr.70018 source
The Discussion (page not provided) reports that nursing students had favorable perceptions of AI and high intentions to adopt it. That supports a related point about students’ willingness to use AI, but it does not establish moderate AI readiness or report readiness levels among practicing nurses. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
PremisecitationEffective decision-making requires data literacy, ethical judgment, risk evaluation, and the ability to manage complexity under pressure.
“The nurse manager literature emphasizes that effective decision-making requires data literacy, ethical judgment, risk evaluation, and the ability to manage complexity under pressure, competencies that are suitable for advanced simulation-based learning activities [12].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [12] Gonzalez-Garcia A, Marques-Sanchez P, Pinto-Carral A, Leiros-Rodriguez R, Perez-Rivera J, Perez-González S. Characterizing the decision-making competency of nurse managers: a scoping review. J Nurs Manag. 2025, 2025:2771210. 10.1155/jonm/2771210 source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe intervention was described in accordance with reporting principles for randomized trials of nonpharmacologic treatments and the TIDieR framework.
“The intervention was described in accordance with reporting principles for randomized trials of nonpharmacologic treatments and the TIDieR framework [15] to enhance clarity, reproducibility, and transparency.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [15] Hoffmann TC, Glasziou PP, Boutron I, et al.. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014, 348:g1687. 10.1136/bmj.g1687 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSome studies suggest that human-led interaction may remain more effective for selected communication-related learning outcomes.
“Nevertheless, some studies suggest that human-led interaction may remain more effective for selected communication-related learning outcomes [3,4,10].”
Quote in the paperNot a substantial claim2 of 3 sources checked- cites [3] Bozkurt SA, Aydoğan S, Dursun Ergezen F, Türkoğlu A. A systematic review and sequential explanatory synthesis: artificial intelligence in healthcare education, a case of nursing. Int Nurs Rev. 2025, 72:70018. 10.1111/inr.70018 source
The Results from Quantitative Studies (Theme 2: Skills; page not available) report that students using a standardized patient scored significantly higher on breast self-examination skills than students using AI. That supports a human-led advantage for one learning outcome, but it is not a communication-related outcome; the paper instead reports improved communication skills with AI. (LLM, conf=0.78)
Experimental — accuracy not yet measured - cites [4] Jiang H, Wang Z, Shen W, Meng M, Yang D, Li X, Hao Y. AI-powered simulation for nursing education: mixed methods systematic review. J Med Internet Res. 2026, 28:e95167. 10.2196/95167 source
- cites [10] Liaw SY, Tan JZ, Lim S, et al.. Artificial intelligence in virtual reality simulation for interprofessional communication training: mixed method study. Nurse Educ Today. 2023, 122:105718. 10.1016/j.nedt.2023.105718 source
Probably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.
Findingown dataThe intervention group's mean perceived usefulness of the AI system was 4.40 (SD 0.60).
“| Perceived usefulness | Perceived usefulness | Perceived usefulness | Perceived usefulness | 4.40 ± 0.60 | 4.40 ± 0.60 | 4.40 ± 0.60 | 4.40 ± 0.60 |”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.
Findingown dataThe intervention group's mean perceived ease of use of the AI system was 4.20 (SD 0.70).
“| Perceived ease of use | Perceived ease of use | Perceived ease of use | Perceived ease of use | 4.20 ± 0.70 | 4.20 ± 0.70 | 4.20 ± 0.70 | 4.20 ± 0.70 |”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe between-group differences in change were statistically significant for leadership performance, team performance, and general self-efficacy.
“The between-group differences in change were statistically significant for all three outcomes.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAt Week 10, the intervention group demonstrated greater improvement in leadership performance than the control group (between-group difference in change = 5.20, 95% CI 4.18-6.22, p < 0.001), with a large standardized effect (Cohen's d = 1.78).
“At Week 10, the intervention group demonstrated greater improvement in leadership performance than the control group (between-group difference in change = 5.20, 95% CI 4.18-6.22, p < 0.001), with a large standardized effect (Cohen's d = 1.78).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe study used a randomized controlled study with a parallel-group, pretest-posttest design.
“This study used a randomized controlled study with a parallel-group, pretest-posttest design.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningFollow-up assessment was limited to the immediate post-intervention period, so longer-term retention and transfer of leadership competencies to clinical practice were not assessed.
“Fourth, follow-up assessment was limited to the immediate post-intervention period; therefore, the longer-term retention and transfer of leadership competencies to clinical practice were not assessed.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataThe C-SEI demonstrated high internal consistency (Cronbach's α = 0.91) and inter-rater reliability (ICC = 0.89, 95% CI 0.84-0.93).
“The C-SEI demonstrated high internal consistency and inter-rater reliability”
- Also stated in: § Results · plain
“| Creighton Simulation Evaluation Instrument (C-SEI) | 23 | 0.91 | 0.89 | (0.84, 0.93) |”
Quote in the paperNot a substantial claim- Also stated in: § Results · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataTwenty-one clinical preceptors served exclusively as trained and blinded outcome assessors and were not included in the randomized sample, sample-size assessment, or participant-level analyses.
“An additional 21 clinical preceptors served exclusively as trained and blinded outcome assessors and were not included in the randomized sample, sample-size assessment, or participant-level analyses.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.
Study design and conductown dataThe AI Acceptance Scale demonstrated acceptable to high internal consistency (Cronbach's α = 0.90).
“| Technology Acceptance Model (AI Acceptance Scale) | 10 | 0.90 | Not analyzed | Not applicable |”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataNo missing observations were identified for the primary or secondary outcome measures; consequently, no missing-data imputation was required.
“No missing observations were identified for the primary or secondary outcome measures; consequently, no missing-data imputation was required.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningThe C-SEI was not developed as a leadership-specific instrument; its use in this study should be interpreted as measuring leadership-related clinical performance rather than the full multidimensional construct of nursing leadership.
“The C-SEI was not developed as a leadership-specific instrument; therefore, its use in this study should be interpreted as measuring leadership-related clinical performance rather than the full multidimensional construct of nursing leadership.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningThe sample consisted predominantly of final-year nursing students, with relatively few clinical preceptors, so the findings may not be fully applicable to experienced nurse leaders or nurses working in diverse clinical contexts.
“Second, the sample consisted predominantly of final-year nursing students, with relatively few clinical preceptors. Therefore, the findings may not be fully applicable to experienced nurse leaders or nurses working in diverse clinical contexts.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenClinical preceptors serving as outcome assessors were blinded to group assignment and trained to apply standardized evaluation criteria.
“Clinical preceptors serving as outcome assessors were blinded to group assignment and trained to apply standardized evaluation criteria.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataThe control and intervention groups were comparable in age and gender at baseline, with no statistically significant differences.
“The groups were comparable in terms of age and gender at baseline, with no statistically significant differences observed between groups.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataThe high level of AI acceptance reported by intervention participants is relevant to the implementation of AI-supported educational strategies.
“The high level of AI acceptance reported by intervention participants is also relevant to the implementation of AI-supported educational strategies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataA post hoc sensitivity assessment was conducted using the observed effect size for the primary outcome, leadership performance measured using the C-SEI.
“A post hoc sensitivity assessment was conducted using the observed effect size for the primary outcome, leadership performance measured using the Creighton Simulation Evaluation Instrument (C-SEI) [14].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Parsons H, Hercinger T, Manz F. Improvement in scoring consistency for the Creighton Simulation Evaluation Instrument©. Clin Simul Nurs. 2012, 8:233-8. source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe C-SEI may not capture the full multidimensional construct of nursing leadership.
“Nevertheless, the C-SEI may not capture the full multidimensional construct of nursing leadership.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductown dataThe 130 nursing students were randomly allocated to either the intervention group (n = 65) or the control group (n = 65).
“The students were randomly allocated to either the intervention group (n = 65) or the control group (n = 65).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
PremisecitationLeadership development remains an area requiring further attention in undergraduate nursing education.
“Leadership development remains an area requiring further attention in undergraduate nursing education”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Høegh-Larsen AM, Gonzalez MT, Reierson IÅ, Husebø SI, Ravik M. Changes in nursing students' self-reported professional competence in simulation-based education and clinical placement: a longitudinal study. Nurse Educ Today. 2022, 119:105592. 10.1016/j.nedt.2022.105592 source
May combine two claims.
Study design and conductreasoningThe study may have been insufficiently powered to detect smaller effects and for reliable role-specific subgroup analyses.
“Consequently, the study may have been insufficiently powered to detect smaller effects and for reliable role-specific subgroup analyses.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataInter-rater reliability for leadership-performance scores was assessed using the ICC, and the ICC was 0.89 (95% CI = 0.84-0.93) for C-SEI scores.
“Inter-rater reliability for leadership-performance scores was assessed using the intraclass correlation coefficient (ICC). The ICC was 0.89 (95% CI = 0.84-0.93) for C-SEI scores.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe allocation sequence was stored in a password-protected file and remained inaccessible to the investigator responsible for participant enrollment and assignment until enrollment was completed, thereby reducing the risk of selection bias.
“The allocation sequence was stored in a password-protected file and remained inaccessible to the investigator responsible for participant enrollment and assignment until enrollment was completed, thereby reducing the risk of selection bias.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataThe AI Acceptance Scale showed a KMO measure of sampling adequacy of 0.89 and a statistically significant Bartlett's test of sphericity (p < 0.001).
“The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, developed by Kaiser and Rice [19], was 0.89. Bartlett’s test of sphericity was statistically significant (p < 0.001).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [19] Kaiser HF, Rice J. Little Jiffy, Mark Iv. Educ Psychol Meas. 1974, 34:111-7. source
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenEligible nursing students were recruited before group allocation and subsequently assigned to the intervention or control group using a computer-generated randomization sequence.
“Eligible nursing students were recruited before group allocation and subsequently assigned to the intervention or control group using a computer-generated randomization sequence.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.
Study design and conductown dataThe General Self-Efficacy Scale demonstrated acceptable to high internal consistency (Cronbach's α = 0.87).
“| General Self-Efficacy Scale | 10 | 0.87 | Not analyzed | Not applicable |”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe outcomes were assessed in a high-fidelity simulation environment, which cannot fully replicate the complexity, unpredictability, and interpersonal demands of actual clinical practice.
“Third, the outcomes were assessed in a high-fidelity simulation environment, which cannot fully replicate the complexity, unpredictability, and interpersonal demands of actual clinical practice.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe AI Acceptance Scale demonstrated good internal consistency in the present sample (Cronbach's α = 0.90).
“The instrument demonstrated good internal consistency in the present sample (Cronbach’s α = 0.90).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningAlthough outcome assessment was blinded, the lack of participant blinding may have influenced engagement and self-efficacy outcomes.
“Although outcome assessment was blinded, the lack of participant blinding may have influenced engagement and self-efficacy outcomes.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenSix trained simulation facilitators delivered the simulation sessions according to the assigned study condition and were not involved in outcome assessment.
“Six trained simulation facilitators delivered the simulation sessions according to the assigned study condition and were not involved in outcome assessment.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe C-SEI was originally developed as a simulation-based clinical performance assessment instrument rather than as a nursing leadership-specific measure.
“Sixth, the C-SEI was originally developed as a simulation-based clinical performance assessment instrument rather than as a nursing leadership-specific measure.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Traditional simulation-based education often relies on fixed scenarios with limited adaptability and minimal real-time decision support.
“Traditional simulation-based education, while effective, often relies on fixed scenarios with limited adaptability and minimal real-time decision support.”
The paper relies on this without support.
AI-enhanced simulation offers a potential solution by creating responsive, adaptive learning environments that support leadership development.
“AI-enhanced simulation offers a potential solution by creating responsive, adaptive learning environments that support leadership development.”
The paper relies on this without support.
AI-enhanced simulation and AI-driven decision-support systems are commonly examined separately.
“AI-enhanced simulation and AI-driven decision-support systems are commonly examined separately”
The paper relies on this without support.
Limited attention has been directed toward the transition from education to clinical practice, including the role of clinical preceptors.
“In addition, limited attention has been directed toward the transition from education to clinical practice, including the role of clinical preceptors.”
The paper relies on this without support.
The AI exposure variable used in the regression validly measures exposure to the AI intervention.
“The model included AI exposure and self-efficacy as predictor variables.”
“| AI exposure | 3.12 | 0.45 | 0.52 | 6.93 | <0.001 |”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Conclusion names a simulation control condition that did not occur
MajorThe conclusion compares the intervention with standard high-fidelity simulation. The methods instead say the control group received conventional clinical placement without simulation scenarios.
“Compared with standard high-fidelity simulation, the intervention group demonstrated higher leadership-performance scores, faster responses to clinical situations, fewer clinical errors, and greater improvement in self-efficacy.”
“Participants in the control group continued with their conventional clinical placement. Neither simulation scenarios nor AI-based decision-support tools were provided.”
Regression degrees of freedom imply more students than were analyzed
MajorThe reported F(3, 147) has 147 residual degrees of freedom, implying 151 observations in a three-predictor regression with an intercept. The paper states that the randomized sample comprised 130 students and that the preceptors were excluded from participant-level analyses.
“| F(3, 147) | F(3, 147) | F(3, 147) | 45.18 | 45.18 | 45.18 |”
“The randomized study sample comprised 130 final-year nursing students. An additional 21 clinical preceptors served exclusively as trained and blinded outcome assessors and were not included in the randomized sample, sample-size assessment, or participant-level analyses.”
Self-efficacy scores conflict between Tables 3 and 4
MajorTable 4 gives different pretest and posttest self-efficacy means for both groups than Table 3 gives for Week 1 and Week 10. The two tables therefore report incompatible changes for the same outcome and groups.
“| Self-efficacy | Control | 21.30 ± 4.20 | 25.10 ± 3.90 | 2.70 (74) | 0.008 | 0.89 | (0.41, 1.37) | | Self-efficacy | Intervention | 21.70 ± 4.50 | 30.40 ± 3.60 | 11.45 (75) | < 0.001 | 1.99 | (1.58, 2.40) |”
“| General self-efficacy (GSES) | 27.80 ± 4.60 | 29.10 ± 4.40 | 28.00 ± 4.50 | 34.60 ± 3.90 | 5.30 | 3.82-6.78 | <0.001 | 1.37 |”
Regression predictor list omits a predictor shown in Table 5
MinorThe methods say the regression included AI exposure and self-efficacy, but Table 5 also includes clinical experience as a predictor.
“The model included AI exposure and self-efficacy as predictor variables.”
“| Clinical experience | 0.84 | 0.38 | 0.21 | 2.21 | 0.03 |”
CONSORT statement year conflicts with its cited reference
MinorThe methods identify reference 13 as the CONSORT 2025 statement, but reference 13 is titled "CONSORT 2010 statement" and is dated 2010.
“The study was reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) 2025 statement for randomized trials [13].”
“13. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials BMJ Schulz KF Altman DG Moher D 0 340 2010 https://doi.org/10.1136/bmj.c332 PMC3043330 21350618”
AI acceptance instrument is described as both modified and unmodified
MinorThe methods say the TAM item pool was modified into a study-specific 10-item questionnaire, but later say the AI Acceptance Scale was used without modification.
“The original TAM item pool was modified to a 10-item version for the present study while retaining the two core TAM constructs.”
“The C-SEI, TeamSTEPPS Team Performance Observation Tool, GSES, and AI Acceptance Scale were accessed from publicly available sources and used without modification, with appropriate citation and acknowledgment of the original developers/authors.”
Narrative explained variance differs from Table 5
MinorThe results say the regression explained 46% of leadership-performance variance, whereas Table 5 reports R2 = 0.48, or 48%.
“AI exposure and self-efficacy were significant predictors of Week 10 leadership performance, and the overall model explained 46% of the variance in leadership performance.”
“| R² | R² | R² | 0.48 | 0.48 | 0.48 |”
Table 4 self-efficacy test degrees of freedom do not match group sizes
MinorTable 4 reports degrees of freedom of 74 and 75 for the control and intervention pretest-posttest comparisons. Each group has 65 students, so a paired t-test using all group members would have 64 degrees of freedom.
“| Self-efficacy | Control | 21.30 ± 4.20 | 25.10 ± 3.90 | 2.70 (74) | 0.008 | 0.89 | (0.41, 1.37) | | Self-efficacy | Intervention | 21.70 ± 4.50 | 30.40 ± 3.60 | 11.45 (75) | < 0.001 | 1.99 | (1.58, 2.40) |”
“The students were randomly allocated to either the intervention group (n = 65) or the control group (n = 65).”
Week 10 follow-up is called immediate post-intervention
MinorThe intervention sessions occurred in the second week of clinical training, while the outcome assessment occurred in Week 10. Describing that follow-up as limited to the immediate post-intervention period conflicts with the stated schedule.
“Fourth, follow-up assessment was limited to the immediate post-intervention period; therefore, the longer-term retention and transfer of leadership competencies to clinical practice were not assessed.”
“At the 10th week of clinical training, all nursing students completed the post-intervention assessment.”
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 1× · see citing works
“Additionally, AI acceptance was assessed using an adapted 10-item questionnaire based on the Technology Acceptance Model (TAM) proposed by Davis [18], administered immediately after the intervention.”
Hoffmann, T. C., Glasziou, P. P., Boutron, I., Milne, R., Perera, R., Moher, D., Altman, D. G., Barbour, V., Macdonald, H., Johnston, M., et al. (2014) · BMJ · doi:10.1136/bmj.g1687 · Cited by 10,548 · cited 1× · see citing works
“The intervention was described in accordance with reporting principles for randomized trials of nonpharmacologic treatments and the TIDieR framework [15] to enhance clarity, reproducibility, and transparency.”
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
Schulz, K. F., Altman, D. G., Moher, D., & for the CONSORT Group (2010) · BMJ · doi:10.1136/bmj.c332 · Cited by 9,329 · see citing works
Kaiser, H. F., & Rice, J. (1974) · Educational and Psychological Measurement · doi:10.1177/001316447403400115 · Cited by 3,423 · cited 1× · see citing works
“The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, developed by Kaiser and Rice [19], was 0.89.”
InfluentialLiaw, S. Y., Tan, J. Z., Lim, S., Zhou, W., Yap, J., Ratan, R., Ooi, S. L., Wong, S. J., Seah, B., & Chua, W. L. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105718 · Cited by 223 · see citing works
El Arab, R. A., Alshakihs, A. H., Alabdulwahab, S. H., Almubarak, Y. S., Alkhalifah, S. S., Abdrbo, A., Hassanein, S., & Sagbakken, M. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1666005 · Cited by 113 · cited 2× · see citing works
“However, nursing students and practicing nurses often report moderate levels of AI readiness, and AI adoption is influenced by perceived usefulness, self-efficacy, infrastructure, and leadership support [2,3].”
Görücü, S., Türk, G., & Karaçam, Z. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106270 · Cited by 94 · cited 1× · see citing works
“Recent evidence indicates that simulation can improve clinical decision-making, whereas leadership-focused simulation can strengthen delegation, communication, teamwork, and problem-solving skills required for safe care delivery [5,6].”
Bozkurt, S. A., Aydoğan, S., Dursun Ergezen, F., & Türkoğlu, A. (2025) · International Nursing Review · doi:10.1111/inr.70018 · Cited by 63 · see citing works
Artificial intelligence tools utilized in nursing education: incidence and associated factors
Jallad, S. T., Alsaqer, K., Albadareen, B. I., & Al-maghaireh, D. (2024) · Nurse Education Today · doi:10.1016/j.nedt.2024.106355 · Cited by 58 · cited 1× · see citing works
“Nursing learners generally report more favorable responses when AI tools are clearly relevant to clinical practice and introduced with appropriate guidance and support [2,10,23].”
Atalla, A. D. G., El-Gawad Mousa, M. A., Hashish, E. A. A., Elseesy, N. A. M., Abd El kader Mohamed, A. I., & Sobhi Mohamed, S. M. (2025) · BMC Nursing · doi:10.1186/s12912-025-03306-3 · Cited by 58 · see citing works
Fung, T. C. J., Chan, S. L., Lam, C. F. M., Lam, C. Y., Cheng, C. C. W., Lai, M. H., Ho, C. C. J., Au, S. L., Mak, L. Y., Hu, S., et al. (2025) · BMC Nursing · doi:10.1186/s12912-025-03492-0 · Cited by 48 · cited 2× · see citing works
“However, rigorous experimental research integrating AI-adaptive simulation with decision-support tools in nursing leadership education remains limited [4,9].”
Labrague, L. J. (2021) · Sultan Qaboos University Medical Journal · doi:10.18295/squmj.4.2021.007 · Cited by 39 · see citing works
Høegh-Larsen, A. M., Gonzalez, M. T., Reierson, I. Å., Husebø, S. I. E., & Ravik, M. (2022) · Nurse Education Today · doi:10.1016/j.nedt.2022.105592 · Cited by 26 · cited 1× · see citing works
“Leadership development remains an area requiring further attention in undergraduate nursing education, and the transfer of simulation learning to clinical practice is not always automatic, supporting the need for more targeted and realistic leadership training [7].”
Improvement in scoring consistency for the Creighton Simulation Evaluation Instrument©
Parsons, M. E., Hawkins, K. S., Hercinger, M., Todd, M., Manz, J. A., & Fang, X. (2012) · Clinical Simulation in Nursing · doi:10.1016/j.ecns.2012.02.008 · Cited by 25 · cited 2× · see citing works
“A post hoc sensitivity assessment was conducted using the observed effect size for the primary outcome, leadership performance measured using the Creighton Simulation Evaluation Instrument (C-SEI) [14].”
Chen, P. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2025.106968 · Cited by 11 · see citing works
Cole, L., Boss, L., Fowler, D., & Cheatham, L. (2021) · Nurse Leader · doi:10.1016/j.mnl.2020.08.012 · Cited by 10
Characterizing the decision-making competency of nurse managers: a scoping review
Gonzalez-Garcia, A., Marques-Sanchez, P., Pinto-Carral, A., Leiros-Rodriguez, R., Perez-Rivera, J., & Perez-González, S. (2025) · Journal of Nursing Management · doi:10.1155/jonm/2771210 · Cited by 8 · cited 1× · see citing works
“The nurse manager literature emphasizes that effective decision-making requires data literacy, ethical judgment, risk evaluation, and the ability to manage complexity under pressure, competencies that are suitable for advanced simulation-based learning activities [12].”
Generative AI adaptive narratives to enhance nursing diagnostic reasoning: a classroom innovation
Díaz, M. J. F. (2026) · BMC Nursing · doi:10.1186/s12912-026-04359-8 · Cited by 8 · see citing works
AI-Powered Simulation for Nursing Education: Mixed Methods Systematic Review
Jiang, H., Wang, Z., Shen, W., Meng, M., Yang, D., Li, X., & Hao, Y. (2026) · Journal of Medical Internet Research · doi:10.2196/95167 · Cited by 4 · see citing works
Teaching with intelligence; AI-enhanced pedagogies in nursing education: a scoping review
Nugent, L., Murray, B., Moore, Z., Patton, D., O’Connor, T., Watson, C., Walsh, K., Renjith, V., & George, J. (2026) · Nurse Education in Practice · doi:10.1016/j.nepr.2026.104878 · Cited by 4 · see citing works
Eid, H. R., Raaen, A. H., Grevskott, T., Bjørgum, M., Myrvold, L., Øvereng, I., & Lysfjord, E. M. (2026) · Journal of Healthcare Leadership · doi:10.2147/jhl.s604428 · Cited by 1 · cited 1× · see citing works
“Qualitative evidence further suggests that realistic leadership scenarios can help nursing students clarify their leadership approaches, strengthen confidence in their decisions, and prepare for workplace complexity [6,21,22].”
Salem, M. F. A., Sharaf, A. K., & Younis, A. A. A. (2026) · Nurse Education in Practice · doi:10.1016/j.nepr.2026.104904 · Cited by 1 · cited 2× · see citing works
“Similar findings have been reported in recent nursing studies of adaptive ECG simulation and AI-enhanced diagnostic narratives, in which intervention participants demonstrated better decision-making, diagnostic accuracy, and self-efficacy than control participants [8,11].”
Flagged first, then by citations
- Authors
- Almalki, M.
- Affiliations
- Taif University (SA)
- Corresponding author
- Almalki, Mohammed
- Venue
- Cureus
- Published
- 2026-10-01
- Volume
- 18
- Issue
- 10
- Pages
- e117315-e117315
- ISSN
- 2168-8184 (ISSN-L)
- PMC
- PMC13629832
- OpenAlex
- W7215099313
- Cited by
- 0
- References
- 21
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Cureus (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Keywords
- artificial intelligence, adaptive simulation, nursing leadership, nursing education, simulation-based training, leadership performance
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