Experimental and quasi-experimental designs that have been used to evaluate the effectiveness of simulation training on nursing students’ confidence, knowledge, and skills were searched for in this study. Articles published in languages other than English were excluded.

Pre-licensure students at all levels of nursing programs, including Bachelor of Science in Nursing and Entry Level Master of Science in Nursing. Nursing programs include those in the Sultanate of Oman, Jordan, Türkiye, South Korea, Kingdom of Saudi Arabia, and China.

Experimental and quasi-experimental studies that focused on at least one of the following were included in the review: 1) nursing students’ skills, 2) knowledge, and 3) confidence.

To develop search strategies for the studies, PICOT framework was used [16]. The study population included prelicensure nursing programs in various universities. The intervention of interest was a high-fidelity simulation compared with traditional clinical training. The primary outcomes assessed were the confidence, knowledge, and skills of student nurses during their training. The initial search was conducted on EBSCO host, Google Scholar, ProQuest, Medline, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases, looking for articles up to August 2025, using string: “(prelicensure nursing programs) OR (high-fidelity simulation) OR (student nurses’ confidence) OR (student nurses’ knowledge) OR (student nurses’ skills)”. Three independent researchers (PO, JB, and SL) performed the systematic search, and the institution’s Librarian (EK) provided guidelines. The search results were independently reviewed to determine inclusion. Titles and abstracts were reviewed, and if eligibility was uncertain, the full text was screened. A manual search of the reference lists was also performed on all screened articles using Joana Briggs Institute (JBI) critical appraisal tool [17]. The study was approved by the University’s Institutional Review Board (IRB2024-207).

The inclusion criteria were articles that examined best practices in HFS-based healthcare education, focused on pre-licensure nursing programs, were peer-reviewed and published between 2014 and 2025, and used either experimental or quasi-experimental designs. Duplicate articles, inaccessible full texts, case reports, mixed-methods studies, case reviews, studies published before 2014, studies focusing on post-licensure nursing programs, or studies using qualitative research methods were excluded from the review.

Two authors (PO and SL) independently selected studies that met the eligibility criteria. Three authors (SL., JB., and PO.) checked each text completely using the Joanna Briggs Institute (JBI) critical appraisal checklist in the final process [17]. Each statement offered four response options: Yes, No, Not Applicable, and Unclear. A “Yes” response was assigned a score of 1, while all other options received a score of 0. Following the evaluation, studies with a JBI score below 70% were excluded. Subsequently, the authors reviewed the selection outcomes to identify any discrepancies. Ultimately, all authors discussed again to reach a unanimous agreement, with no conflicting opinions regarding the suitability of the included studies. Figure 1 shows the PRISMA 2021 [18] flow diagram. From the 1767 records identified, 632 remained after duplicate, non-primary research, and articles not focused on nursing students were removed. After they were screened by title and abstract, 240 records were excluded as not relevant. Among the 392 articles sought for retrieval, 130 were not retrieved. Of the 262 full-text articles that met criteria, 256 were excluded due to JBI below 70%, belonged to case reports, belonged to case reviews, had a wrong study design, and had a high risk of bias. Six studies with low to moderate risk were included in the review.

Figure 1. Flow chart of the study selection process.

To inform the appropriateness of including studies in the meta-analysis and facilitate assessment of the strength of the evidence we assessed the risk of bias in each included study using the Cochrane Collaboration’s Risk of Bias tool [19][20]. Three reviewers (PO, JB, and SL) completed this independently and agreed on final assessments. ROBINS-I tool for non-randomized studies [21] consists of 7 domains: Bias due to confounding, bias in the selection of participants into the study, bias in classification of interventions, bias due to deviations from intended interventions, bias due to missing data, bias in measurement of the outcome, and bias in selection of the reported result. Discrepancies in the assessment results were discussed and reviewed by all authors before a consensus decision. The studies were on the low risk on standard scale comparable to high-quality randomized trial, and very few on moderate risks were also considered sound evidence for a non-randomized study (see Figure 2).

Figure 2. Risk of bias domains.

All authors agreed to use Google Drive to verify data accuracy. The first author extracted details from eligible studies into a spreadsheet. All the authors checked the data in the spreadsheet to ensure data integrity. Missing details were addressed through comments on Google Drive.

Data were entered into SPSS version 31 software [22] by the first author. The effect size, weight, standard deviation, and 95% confidence interval (CI) were calculated for each study. The overall effect size, no effect value, and overall effect size value from pooled studies were also calculated on forest plot. Homogeneity and heterogeneity between the combined studies were tested using a standardized chi-square test. Homogeneity was <0.001, and heterogeneity was significantly high with I² of 76% - 88%. A P-value of <0.10 for the test of heterogeneity triggered the use of a random-effects model with all analyses.

This systematic review included six primary studies examining the effectiveness of HFS in pre-licensure nursing programs on student nurses’ knowledge, skills, and confidence (Table 1). The selected six studies with 402 nursing students from various universities compared the effects of high-fidelity simulation and traditional teaching methods on nursing students’ confidence, knowledge, and skills. Geographically, the studies were conducted in the Sultanate of Oman, Jordan, Türkiye, South Korea, Kingdom of Saudi Arabia, and China, all the six countries within five regions. [15] had 74 students (40 in the TCT group and 34 in the TCT + HFS group) from a cohort of nursing students and compared the effects of a combination of traditional clinical training with high-fidelity simulation (TCT + HFS) activities vs TCT alone on the clinical competency and knowledge among students enrolled in a maternity nursing course. [23] had 75 nursing students from Arab American University Palestine and An-Najah National University and evaluated the efficacy of employing high-fidelity simulation (HFS) in enhancing mental health nursing students’ practice, satisfaction, and self-confidence in contrast to a group of students undergoing traditional nursing education methods.

Table 1. Main characteristics of the studies included in the review.

[10] had 52 senior nursing students enrolled at a state university in Ankara. Participants completed a pre-test and were randomly assigned to either high-fidelity simulation group or an e-learning group, with 26 students in each, and the study evaluated the impact of e-learning and high-fidelity simulation methods on the self-efficacy of nursing students. In [14], the participants were 52 students who had enrolled in the course, and the study applied VS (virtual simulation) and HFS (high-fidelity simulation) in a different order to determine differences in 1) the problem-solving process, 2) clinical reasoning, 3) reflective thinking, 4) satisfaction with the practicum, and 5) self-confidence. [13] with 81 students compared the effects of “conventional TBL” (CTBL) and “TBL combined with high fidelity simulation” on student’s attitude and test performance. With [12], the participants were 68 full-time third-year nursing students, and the study evaluated the development of nursing students’ clinical thinking after participating in clinical simulation and compared the acquisition of nursing skills with students from previous years (see Table 1). All participants were fully enrolled undergraduate students from seven universities, and with a range of 2 - 4 years as nursing students (see Table 2).

Table 2. Demographic characteristics of participants in the study.

To answer the research question on the impact of high-fidelity simulation on student nurses’ confidence, knowledge, and skills, the following analyses were performed. On knowledge, [15] was not statistically significant at 0.25, 95% CI [−0.21, 0.71], P = 0.29, and [10] was not statistically significant at −0.27, 95% CI [−0.82, 0.28], P = 0. 33. However, the overall pool of the studies on the effect of high-fidelity simulation on knowledge was statistically significant, a random-effects meta-analysis revealed a significant positive effect, 0.74, 95% CI [0.16, 1.31], P = 0.01 (see Figure 3). The forest plot on the effect of high-fidelity simulation on skills was statistically significant, a random-effects meta-analysis revealed a significant positive effect, 0.70, 95% CI [0.17, 1.23], P = 0.01 (see Figure 4), while the independent few studies were not statistically significant as follows: [15] was not statistically significant at −0.08, 95% CI [−0.38, 0.54], P = 0.74 , and [10] was not statistically significant at 0.28, 95% CI [−0.45, 0.64], P = 0.73, [14] was not statistically significant at 0.53, 95% CI [−0.03, 1.08], P = 0.06 (see Figure 4). The overall pool of the five studies on the effect of high-fidelity simulation on confidence was statistically significant, a random-effects meta-analysis revealed a significant positive effect, 0.60, 95% CI [0.17, 1.03], P = 0.01 (see Figure 5). However, [10] was not statistically significant at −0.14, 95% CI [−0.69, 0.40], P = 0.60.

Comparison of high-fidelity simulation and conventional traditional learning influence on skills was shown in Figure 5.

Figure 3. Comparison of high-fidelity simulation and conventional traditional learning influence on knowledge.

Figure 4. Comparison of high-fidelity simulation and conventional traditional learning influence on skills.

Figure 5. Comparison of high-fidelity simulation and conventional traditional learning influence on confidence.

A random-effects model was applied to address the heterogeneity observed in the

Table 3. Heterogeneity measures on confidence and skills.

study. The subgroup analysis focused on comparison of high-fidelity simulation versus traditional learning methods on both student nurses’ confidence and skills. The heterogeneity of the subgroup revealed 76.1% on confidence, 86.4% on skills, and 81.6% for the overall (Table 3). The overall pool effect-size was statistically significant, random-effects meta-analysis revealed a significant positive effect, 0.65, 95% CI [0.32, 0.99], P < 0.01 (Figure 6). The effect size of the subgroup was found to be statistically significant (2-tailed P < 0.01 (Table 4).

Table 4. Effect size estimates for subgroup analysis.

Figure 6. Test of between-subgroup homogeneity.

This review supports the effectiveness of high-fidelity simulation in prelicensure nursing education. According to the American Organization of Nurse Executives, the gap between nursing education and practice will not be bridged until baccalaureate curricula address the complexities and rapid changes in nursing practice [29]. It has been suggested that nursing education requires radical revisions to meet the knowledge and skills demands of the profession. Students’ skills, knowledge, and confidence can be significantly enhanced through simulation. Human patient simulation has gained widespread acceptance for training healthcare professionals at all levels. With limited clinical sites, faculty shortages, and ethical and safety concerns, simulation-based clinical education has become the best instructional way for nursing students to develop clinical and decision-making skills without compromising the patient’s well-being [30].

An increasing focus on quality, acuity, safety, and specialized and complex clinical practice emphasizes the need to incorporate innovative experiential learning. Many patient situations can be replicated using high-fidelity simulation to help students develop their nursing skills and competency. High-fidelity simulation bridges the gap between knowledge and clinical practice, especially when clinical opportunities are limited. It can be incorporated through role-playing, computer-assisted instruction, task training, and low- to high-fidelity manikins. The use of high-fidelity simulation in nursing education provides opportunities to practice clinical reasoning skills in controlled environments [31]. High-fidelity simulation is widely noted in healthcare as being realistic and positively contributing to skill acquisition within registered nursing education [7].

Simulations also increase satisfaction and confidence levels; however, these factors require further study among student nurses. High-fidelity simulation has been described as an innovative teaching tool used in healthcare education to support learning and prepare nurses to provide safe and competent care in complex healthcare settings (Williams, 2019) [1].

The overall pooled results of our review demonstrated statistical significance. However, more studies need to be done to find out strategies to enhance the effectiveness of high-fidelity simulation among nursing students. Many studies have supported the effectiveness of high-fidelity simulation in prelicensure nursing programs; however, a gap still exists on how confidence affects the skills and knowledge of nursing students. In most studies, the groups exposed to simulation method achieved better skills and knowledge than those exposed to traditional method, but personal attributes such as intelligence and the infrastructures available in various schools and universities must be taken into consideration. Future research should explore the factors that enhance students’ confidence, satisfaction, skills, and knowledge when using high-fidelity simulation-based learning. Additionally, examining the relationship between confidence and skills could provide more insights into the effectiveness of high-fidelity simulation. As millennial and Generation Z nurses enter the field, further research is needed to identify key factors for improving the implementation of high-fidelity simulation.