HomeJournal of Interdisciplinary Perspectivesvol. 3 no. 10 (2025)

Evaluating the Effectiveness of AI-Generated Health Educational Videos on Nursing Students’ Knowledge Acquisition of the International Patient Safety Goals (IPSG)

Althea Monica P. Alvarado | Rygel M. Aguilar | Joshua Antonio B. Andres | Julius Francis M. Angeles | Maro V. Anicas | Kelsey Pauleena T. Apacible | Geraldine Merylei C. Arcilla | Reden Karl A. Arcilla | Charissa Rosamond D. Calacday

Discipline: Education

 

Abstract:

This study addressed the limited focus on the specific educational needs of undergraduate nursing students in existing AI in healthcare research, particularly in relation to the International Patient Safety Goals (IPSG). While AI-generated health educational videos offer potential benefits, their effectiveness in enhancing understanding of IPSG remains underexplored. A true experimental pretest–posttest design was employed with 60 first-year nursing students from a university in Quezon City, who were selected through stratified random sampling and randomly assigned to either a control or experimental group. A researchermade questionnaire, consisting of a 30-item multiple-choice test and a six-item situational test, was used to measure both knowledge acquisition and practical application. The pretest revealed comparable baseline knowledge levels (control: M = 26.33, SD = 4.11; experimental: M = 27.17, SD = 2.76), both of which were categorized as “Average.” Following the intervention, the experimental group demonstrated a significant improvement (M = 29.63, SD = 2.13), as indicated by a paired-sample t-test yielding a statistically significant t-value of 6.251 (p < .001). These results suggest that AI-generated videos are a valuable supplementary instructional tool in nursing education. Limitations included the short intervention period, absence of longterm retention measures, and the study’s single-institution scope. The contribution of this study lies in demonstrating the potential of AI-generated videos as a transformative approach in nursing education. If validated on a larger scale, these tools could establish a more standardized, scalable, and accessible mode of teaching patient safety concepts. Beyond supporting individual learning, they may address gaps in instructional quality across institutions, promote consistency in patient safety training, and strengthen clinical preparedness among nursing students. In this way, the study provides both theoretical insights into the role of AI in education and practical evidence to inform curriculum development, institutional policies, and future research on technology-enhanced learning in healthcare.



References:

  1. Abd Karim, R. (2023). AI–generated content for education and learning. In AI for seamless education. Pressbooks. https://tinyurl.com/3kbt78jm
  2. Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2023). The effects of artificial intelligence applications in educational settings. Technological Forecasting and Social Change, 194, 123076. https://doi.org/10.1016/j.techfore.2023.123076
  3. Alquwez, N., Cruz, J. P., Alshammari, F., Felemban, E. M., Almazan, J. U., Tumala, R. B., Alabdulaziz, H. M., Alsolami, F., Silang, J. P. B. T., & Tork, H. M. M. (2019). A multi‐university assessment of patient safety competence during clinical training among baccalaureate nursing students: A cross‐sectional study. Journal of Clinical Nursing, 28(9–10), 1771–1781. https://doi.org/10.1111/jocn.14790
  4. Al-Za’areer, M. S., Leong, O. S., Azmi, I. S. M., Alhumaidi, B. N., Elneblawi, N. H., & Eltayeb, M. M. (2023). Exploring the impact of simulation on nursing students’ knowledge and skills during basic and advanced cardiac life support training in Saudi Arabia. Research Journal of Pharmacy and Technology, 5453–5461. https://doi.org/10.52711/0974-360x.2023.00883
  5. American Association of Colleges of Nursing (AACN). (2021). The essentials: Core competencies for professional nursing education. https://tinyurl.com/y6ft84uw
  6. Attia, A. G., Ahmed, E. S., & Safan, S. M. (2021). Nurses’ application of international patient safety goals at accredited and non-accredited hospitals. Journal of Nursing Science – Benha University, 2(2), 129–142. https://doi.org/10.21608/jnsbu.2021.186435
  7. Asenhabi, B. M. (2019). Basics of research design: A guide to selecting an appropriate research design. International Journal of Contemporary Applied Research, 6(5). https://tinyurl.com/53d5khpb
  8. Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from the educational landscape: A review of AI studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800  
  9. Cheng, P. H. W. (2024). The irreplaceable role of teachers over AI: A student’s perspective. Teaching Connections. https://tinyurl.com/2m2fxhck
  10. De Gagne, J. C. (2023). The state of artificial intelligence in nursing education: Past, present, and future directions. International Journal of Environmental Research and Public Health, 20(6), 4884. https://doi.org/10.3390/ijerph20064884
  11. Galmarini, E., Marciano, L., & Schulz, P. J. (2024). The effectiveness of visual-based interventions on health literacy in health care: A systematic review and meta-analysis. BMC Health Services Research, 24(1), Article 11138. https://doi.org/10.1186/s12913-024-11138-1
  12. Gavarkovs, A., Miller, E., Koons, J., Labrecque, M., Moreau, K., & Brydges, R. (2025). Motivation theories and constructs in experimental studies of online instruction: Systematic review and directed content analysis. JMIR Medical Education, 11(1), e64179. https://doi.org/10.2196/64179
  13. Ghasemi, M. R., Moonaghi, H. K., & Heydari, A. (2020). Strategies for sustaining and enhancing nursing students’ engagement in academic and clinical settings: A narrative review. Korean Journal of Medical Education, 32(2), 103–117. https://doi.org/10.3946/kjme.2020.159
  14. Heier, L., Gambashidze, N., Hammerschmidt, J., Riouchi, D., Geiser, F., & Ernstmann, N. (2022). Development and testing of the situational judgement test to measure safety performance of healthcare professionals: An explorative cross-sectional study. Nursing Open. https://pmc.ncbi.nlm.nih.gov/articles/PMC8685870/
  15. Hinkle, J. F. (2023). Personalization and learning outcome in a nursing pathophysiology and pharmacology course: Canvas© mastery adoption pilot study. SAGE Open Nursing, 9. https://doi.org/10.1177/23779608231186030
  16. Hirani, R., Noruzi, K., Khuram, H., Hussaini, A. S., Aifuwa, E. I., Ely, K. E., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., & Etienne, M. (2024). Artificial intelligence and healthcare: A journey through history, present innovations, and future possibilities. Life, 14(5), 557. https://doi.org/10.3390/life14050557
  17. Høegh-Larsen, A. M., Gonzalez, M. T., Reierson, I. Å., Husebø, S. I. E., Hofoss, D., & Ravik, M. (2023). Nursing students’ clinical judgment skills in simulation and clinical placement: A comparison of student self-assessment and evaluator assessment. BMC Nursing, 22(1). https://doi.org/10.1186/s12912-023-01220-0
  18. International Patient Safety Goals. (2023). Joint commission international. https://tinyurl.com/384x4792
  19. Jallad, S. T., Alsaqer, K., Albadareen, B. I., & Al-Maghaireh, D. (2024). Artificial intelligence tools utilized in nursing education: Incidence and associated factors. Nurse Education Today, 142, 106355. https://doi.org/10.1016/j.nedt.2024.106355
  20. Kalinowski, E., Westphal, A., Jurczok, A., & Vock, M. (2024). The essential role of teacher self-efficacy and enthusiasm for differentiated instruction. Teaching and Teacher Education, 137, 104663.
  21.  https://doi.org/10.1016/j.tate.2024.104663
  22. Limna, P., Jakwatanatham, S., Siripipattanakul, S., Kaewpuang, P., & Sriboonruang, P. (2022). A review of artificial intelligence (AI) in education during the digital era. Advance Knowledge for Executives, 1(1), 1–9. https://ssrn.com/abstract=4160798
  23. Matsiola, M., Lappas, G., & Yannacopoulou, A. (2024). Generative AI in education: Assessing usability, ethical implications, and communication effectiveness. Societies, 14(12), 267. https://doi.org/10.3390/soc14120267
  24. Moeyaert, M., Maggin, D. M., & Onghena, P. (2021). The power to explain variability in intervention effectiveness in single-case research using hierarchical linear modeling. SchoPsychology Quarterly, 36(1), 41–53. https://tinyurl.com/4e2w549e
  25. Mohd, Asif., & Kazi, S. (2024). Examining the influence of short videos on attention span and their relationship with academic performance. International Journal of Science and Research, 13(4), 1877–1883. https://doi.org/10.21275/SR24428105200 
  26. Montejo, L., Fenton, A., & Davis, G. (2024). Artificial intelligence (AI) applications in healthcare and considerations for nursing education. Nurse Education in Practice, 80, 104158. https://doi.org/10.1016/j.nepr.2024.104158
  27. Navarrete, E., Hoppe, A., & Ewerth, R. (2021). A review of recent advances in video-based learning research: Video features, interaction, tools, and technologies. https://tinyurl.com/deu75v93
  28. Pehlivan, K., Aslan, E., Karagözoğlu, A., & Yildirim, A. (2022). Determination of the learning styles of nursing students: A descriptive study. International Journal of Caring Sciences, 15(1), 395–405. 
  29. https://tinyurl.com/4vre38j8
  30. Pivač, S., Skela-Savič, B., Jović, D., Avdić, M., & Kalender-Smajlović, S. (2021). Implementation of active learning methods by nurse educators in undergraduate nursing students’ programs: A group interview. BMC Nursing, 20(1). https://doi.org/10.1186/s12912-021-00688-y
  31. Salem, N. A. H. (2020). Exploring student nurses’ preparedness and readiness to care for critically ill patients and implications for patient safety. International Journal of Nursing Education, 13(1), 31–39. https://doi.org/10.37506/ijone.v13i1.13308
  32. Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., & Roepman, R. (2021). Artificial intelligence: A powerful paradigm for scientific research. Innovation, 2(4), 100179. https://doi.org/10.1016/j.xinn.2021.100179