Grit, AI Literacy, and Academic Motivation among Generation Z Allied Health Students of Mercado College: Implications for Outcomes-Based Education
##doi.readerDisplayName##:
https://doi.org/10.65166/f529tt54关键词:
academic motivation, AI literacy, allied health education, Generation Z learners, grit, outcomes-based education摘要
The increasing use of artificial intelligence in education and healthcare requires allied health students to develop not only technical competence but also the motivation and resilience needed to use emerging technologies responsibly. This study examined grit, AI literacy, and academic motivation among Generation Z allied health students of Mercado College and considered their implications for strengthening Outcomes-Based Education (OBE). Using a descriptive-correlational action research design, the study surveyed 230 Generation Z students enrolled in allied health programs. The instrument measured grit in terms of perseverance of effort and consistency of interest; AI literacy in terms of functional knowledge and use, critical evaluation and ethics, and application in learning; and academic motivation in terms of intrinsic motivation, extrinsic motivation, and self-regulation and engagement. The instrument demonstrated excellent internal consistency, with a Cronbach's alpha coefficient of 0.977. Findings showed that students reported high levels of grit, AI literacy, and academic motivation. Grit significantly predicted academic motivation, while AI literacy did not significantly predict academic motivation based on the available regression model. These findings suggest that while students demonstrate functional familiarity with AI tools, their technological competence may not yet be fully connected to their academic drive or professional identity. The study recommends a structured instructional intervention integrating resilience-building, ethical AI literacy, and motivation-enhancing strategies within the OBE curriculum.
##plugins.themes.default.displayStats.downloads##
参考
Atento, R. G. (2025). Exploring e-learning for sustainable development: Integrating SDGs in management education at Philippine higher education institutions. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/2qcjx561
Atento, R. G., Quinto, L., Espelita, C. A. M., & Castaneda, C. (2025). Integrating business and health analytics: A conceptual framework for dual outcomes in healthcare. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/04pdc866
Atento, R. G. O., Quinto, L. F., & Espelita, C. A. M. H. (2025). Bridging global health workforce gaps 2050: A multilevel analysis of global demand, Philippine supply fragilities, and competency alignment. International Journal of Health and Business Analytics, 1(2), 1-30. https://doi.org/10.65166/kgbpey79
Atento, R. G. O., Quinto, L. F., Espelita, C. A. M., & San Juan, F. M. (2025). Narrative health analytics: Integrating empathy, data, and ethics in patient-centered healthcare. International Journal of Health and Business Analytics, 1(2), 1-33. https://doi.org/10.65166/yxgx8e59
Balut, I. M., Baltazar, R. K., Fronda, M., Siador, K., Tandog, P. J. B., & Atento, R. G. (2026). Peer influence experiences and personal-social competencies among late adolescents: Evidence from a private higher education institution in the Philippines. International Journal of Behavioral and Social Analytics, 1(1). https://doi.org/10.65166/magr2j66
Bermido, C. M., Quinto, L. F., & Atento, R. G. O. (2025). A qualitative thematic review of contemporary challenges affecting health professions education: Implications for higher education leadership. International Journal of Health and Business Analytics, 1(2). https://doi.org/10.65166/yfm5w791
Bliss, R., & Jacobson, E. (2020). Doctor of Physical Therapy student grit as a predictor of academic success: A pilot study. Health Professions Education, 6(4), 522-528. https://doi.org/10.1016/j.hpe.2020.06.006
Cayubit, R. F. O. (2022). Why learning environment matters? An analysis on how the learning environment influences the academic motivation, learning strategies and engagement of college students. Learning Environments Research, 25(2), 581-599. https://doi.org/10.1007/s10984-021-09382-x
Chapman, R. M., Chapman, C. E., Johnson, H. E., & Chapman, D. (2026). Perceptions of generative artificial intelligence among biomedical academics with career trajectories in healthcare: A mixed methods study. AI, 7(3), Article 106. https://doi.org/10.3390/ai7030106
Chen, Y., Lv, Z., Li, X., Chen, Y., & Li, W. (2026). Artificial intelligence literacy and academic resilience in undergraduate nursing students: The mediating role of self-efficacy and artificial intelligence anxiety. BMC Nursing, 25, Article 276. https://doi.org/10.1186/s12912-026-04420-6
Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087-1101. https://doi.org/10.1037/0022-3514.92.6.1087
Hopson, S., Mildon, C., Hassard, K., Kubalek, C., Laverty, L., Urie, P., & Della Corte, D. (2025). Enhancing AI literacy in undergraduate pre-medical education through student associations: An educational intervention. BMC Medical Education, 25, Article 999. https://doi.org/10.1186/s12909-025-07556-2
Kalantzis, M., & Cope, B. (2025). Literacy in the time of artificial intelligence. Reading Research Quarterly, 60(1), Article e591. https://doi.org/10.1002/rrq.591
Li, C., Tsuei, S. H.-T., & Wu, H. (2026). AI literacy among Chinese medical students: Cross-sectional examination of individual and environmental factors. JMIR Medical Education, 12, Article e80604. https://doi.org/10.2196/80604
Lolong, J. R. V., Aparejado, A. S. S., Carino, S. Y. M., Valderama, M. L. P., & Atento, R. G. O. (2026). Dark Triad personality traits and academic dishonesty-related tendencies among senior high school students: A correlational study. International Journal of Behavioral and Social Analytics, 1(1). https://doi.org/10.65166/2xe1ga71
Mooradian, T. A., Matzler, K., Uzelac, B., & Bauer, F. (2016). Perspiration and inspiration: Grit and innovativeness as antecedents of entrepreneurial success. Journal of Economic Psychology, 56, 232-243. https://doi.org/10.1016/j.joep.2016.08.001
Pan, Z., Biegley, L. T., Taylor, A. J., & Zheng, H. (2024). A systematic review of learning analytics-incorporated instructional interventions on learning management systems. Journal of Learning Analytics, 11(2), 52-72. https://doi.org/10.18608/jla.2023.8093
Quinto, L. (2026). Resilient health systems, diagnostics, and evidence-based analytics in an era of clinical, community, and economic disruption. International Journal of Health & Business Analytics, 2(1). https://doi.org/10.65166/78fb7195
Rao, L., Tian, Y., & Atento, R. G. O. (2025). Adoption and perceived effectiveness of AI in education: Personalization, outcomes, and equity. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/qgq89291
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
Shorey, S., Chan, V., Rajendran, P., & Ang, E. (2021). Learning styles, preferences and needs of generation Z healthcare students: Scoping review. Nurse Education in Practice, 57, Article 103247. https://doi.org/10.1016/j.nepr.2021.103247
Southworth, J., Migliaccio, K., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, Article 100127. https://doi.org/10.1016/j.caeai.2023.100127
Teng, M., Singla, R., Yau, O., Lamoureux, D., Gupta, A., Hu, Z., Hu, R., Aissiou, A., Eaton, S. E. A., Hamm, C., Hu, S., Kelly, D. J., MacMillan, K., Malik, S., Mazzoli, V., Teng, Y.-W., Laricheva, M., Jarus, T., & Field, T. S. (2022). Health care students' perspectives on artificial intelligence: Countrywide survey in Canada. JMIR Medical Education, 8(1), Article e33390. https://doi.org/10.2196/33390
Urhahne, D., & Wijnia, L. (2023). Theories of motivation in education: An integrative framework. Educational Psychology Review, 35(2), Article 45. https://doi.org/10.1007/s10648-023-09767-9
##submission.downloads##
已出版
期次
栏目
##submission.license##
##submission.copyrightStatement##
##submission.license.cc.by-nc-sa4.footer##This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Authors retain copyright of their articles but grant the Asian Journal of Educational Leadership, Management and Pedagogy (AJELMP) the right of first publication.
Under this license, others may share and adapt the work for non-commercial purposes, provided appropriate credit is given and derivative works are distributed under the same license.
