Perceived Practicality, Perceived Acceptability, and Perceived Educational Effect of AI-Powered Simulation Tools Among Stakeholders of a Nursing School in Kalibo, Aklan

Authors

  • Marivic G. Regidor Saint Bernadette of Lourdes College, Inc. Author

DOI:

https://doi.org/10.5281/zenodo.20260292

Keywords:

AI-powered simulation, nursing education, practicality, acceptability, educational effect, simulation-based learning

Abstract

The integration of artificial intelligence (AI)-powered simulation tools has emerged as a promising strategy for strengthening nursing education, particularly in provincial and resource-limited settings where traditional high-fidelity simulation may be constrained by cost, equipment, and faculty requirements. This descriptive-correlational study examined the perceived practicality, acceptability, and educational effect of AI-powered simulation tools among stakeholders of a nursing school in Kalibo, Aklan. A total of 313 respondents composed of Bachelor of Science in Nursing students, clinical instructors, and faculty members participated through purposive sampling. Data were gathered using a validated and reliable researcher-made questionnaire administered through Google Forms. Descriptive statistics, weighted means, standard deviations, and Pearson correlation analysis were used to analyze the data. Findings showed that stakeholders agreed that the AI-powered simulation tool was practical (M = 4.11), acceptable (M = 4.20), and educationally effective (M = 4.31). The highest ratings emphasized clear instructions, continued use in the nursing program, engagement, patient safety awareness, and the ability to connect theoretical knowledge with clinical scenarios. Age and sex were not significantly associated with stakeholder perceptions, while stakeholder role, clinical exposure setting, familiarity with simulation-based learning, and frequency of simulation use showed significant relationships. The study concludes that AI-powered simulation tools are viable instructional innovations for nursing education in provincial contexts. Institutional integration, improved internet infrastructure, and structured faculty facilitation are recommended to maximize equitable and effective implementation.

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References

Almotairy, M. M., Algabbashi, M., Alshutwi, S., Shibily, F., Alsharif, F., Almutairi, W., & Nahari, A. (2023). Nursing faculty perceptions of simulation culture readiness in Saudi universities: A cross-sectional study. BMC Nursing, 22(1). https://doi.org/10.1186/s12912-023-01278-w

Alshammari, F., Pasay-An, E., Gonzales, F., & Torres, S. (2023). Technology acceptance and digital learning readiness among nursing students: A cross-sectional study. Nurse Education Today, 120, 105649. https://doi.org/10.1016/j.nedt.2022.105649

Bearman, M., & Ajjawi, R. (2025). Artificial intelligence and gender equity: An integrated approach for health professional education. Medical Education. https://doi.org/10.1111/medu.15657

Can’t, R. P., & Cooper, S. J. (2017). Use of simulation-based learning in undergraduate nurse education: An umbrella systematic review. Nurse Education Today, 49, 63-71. https://doi.org/10.1016/j.nedt.2016.11.015

Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499-541. https://doi.org/10.3102/0034654320933544

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

Elendu, C., Amaechi, D. C., Okatta, A. U., Amaechi, E. C., Elendu, T. C., Ezeh, C. P., & Elendu, I. D. (2024). The impact of simulation-based training in medical education: A review. Medicine, 103(27), 1-14. https://doi.org/10.1097/MD.0000000000038813

Foronda, C. L., Fernandez-Burgos, M., Nadeau, C., Kelley, C. N., & Henry, M. N. (2020). Virtual simulation in nursing education: A systematic review spanning 1996 to 2018. Simulation in Healthcare, 15(1), 46-54. https://doi.org/10.1097/SIH.0000000000000411

Hamilton, A. (2024). Artificial intelligence and healthcare simulation: The shifting landscape of medical education. Cureus, 16(5), e59747. https://doi.org/10.7759/cureus.59747

Jeon, J., Kim, J. H., & Choi, E. H. (2020). Needs assessment for a VR-based adult nursing simulation training program for Korean nursing students: A qualitative study using focus group interviews. International Journal of Environmental Research and Public Health, 17(23), 8880. https://doi.org/10.3390/ijerph17238880

Jung, H. (2023). Artificial intelligence in nursing education: A scoping review. Nurse Education Today.

Liaw, S. Y., Ooi, S. W., & Siau, C. (2023). AI-powered virtual reality simulation in nursing education.

Liaw, S. Y., Ooi, S. W., Rusli, K. D. B., Lau, T. C., & Tam, W. W. S. (2023). Nurse educators’ perspectives on the use of virtual simulation in nursing education: A systematic review. Nurse Education Today, 121, 105710. https://doi.org/10.1016/j.nedt.2023.105710

Liu, K., Zhang, W., Li, W., Wang, T., & Zheng, Y. (2023). Effectiveness of virtual reality in nursing education: A systematic review and meta-analysis. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04662-x

Martinez-Ortigosa, C., Porras-Segovia, A., & Marquez, J. (2023). AI-supported simulation for improving clinical reasoning in undergraduate nursing education. Simulation in Healthcare, 18(4), 233-240. https://doi.org/10.1097/SIH.0000000000000640

McCombes, S. (2020, June 19). Descriptive research design: Definition, methods, and examples. Scribbr.

Middleton, F. (2022). Cronbach’s alpha: Definition, calculation, and interpretation. Scribbr. https://www.scribbr.com/statistics/cronbachs-alpha/

Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice. Wolters Kluwer.

Sahin Karaduman, S., & Basak, T. (2024). Effects of high-fidelity simulation on nursing students’ clinical competence: A meta-analysis. Nurse Education Today, 84, 104220. https://doi.org/10.1016/j.nedt.2019.104220

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2022). User acceptance of information technology: Toward a unified view. MIS Quarterly, 46(1), 351-376. https://doi.org/10.25300/

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Published

2026-05-18

How to Cite

Regidor, M. (2026). Perceived Practicality, Perceived Acceptability, and Perceived Educational Effect of AI-Powered Simulation Tools Among Stakeholders of a Nursing School in Kalibo, Aklan. International Journal of Education, Research, and Innovation Perspectives, 2(5), 874-882. https://doi.org/10.5281/zenodo.20260292

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