Kahandaan sa AI, Nakikitang Kaugnayan, at Intensyong Gumamit ng Generative AI ng mga Mag-aaral sa Metro Manila
DOI:
https://doi.org/10.5281/zenodo.20594102Keywords:
Generative Artificial Intelligence, AI Readiness, Perceived Relevance, AI for Social Good, Behavioral Intention, Structural Equation ModelingAbstract
Patuloy na binabago ng generative artificial intelligence ang paraan ng pagtuturo at pagkatuto, ngunit nananatiling hindi pantay ang intensyon ng mga mag-aaral na gamitin ito para sa mga layuning akademiko. Nakabatay sa Unified Theory of Acceptance and Use of Technology, sinuri ng pag-aaral na ito ang mga salik na nakaaapekto sa behavioral intention ng mga mag-aaral na gumamit ng generative AI tools sa Metro Manila. Partikular nitong sinuri ang epekto ng attitude toward using AI, AI for social good, perceived relevance of AI, at AI readiness sa behavioral intention, gayundin ang mediating role ng AI readiness. Gamit ang quantitative causal research design, nakalap ang datos mula sa 498 mag-aaral na may pamilyaridad o karanasan sa paggamit ng generative AI para sa mga gawaing akademiko. Sinuri ang datos gamit ang structural equation modeling. Ipinakita ng mga resulta na ang attitude toward using AI, perceived relevance of AI, at AI readiness ay may makabuluhang positibong epekto sa behavioral intention. Samantala, walang direktang makabuluhang epekto ang AI for social good sa behavioral intention, ngunit makabuluhan itong nakaapekto sa AI readiness. Ganap na namagitan ang AI readiness sa ugnayan ng AI for social good at behavioral intention, at bahagyang namagitan sa ugnayan ng perceived relevance at behavioral intention. Ipinahihiwatig ng mga natuklasan na ang paggamit ng generative AI ng mga mag-aaral ay higit na nahuhubog ng akademikong kaugnayan, positibong saloobin, at kahandaan kaysa sa pangkalahatang pananaw sa panlipunang halaga ng AI.
References
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, Article 100099. https://doi.org/10.1016/j.caeai.2022.100099
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence: Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K–12: A systematic literature review. International Journal of STEM Education, 10, Article 29. https://doi.org/10.1186/s40594-023-00409-7
Chai, C. S. (2022). Perceptions of and behavioral intentions toward learning artificial intelligence among students. Educational Technology & Society, 25(1), 1–13.
Chai, C. S., Lin, P., Jong, M. S., Dai, Y., Chiu, T. K. F., & Huang, B. (2020). Factors influencing students’ behavioral intention to continue artificial intelligence learning. In Proceedings of the 2020 International Symposium on Educational Technology (pp. 1–5). IEEE. https://doi.org/10.1109/ISET49818.2020.00040
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K. F., & Qin, J. (2021). Perceptions of and behavioral intentions toward learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89–101.
Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41(2), 745–783. https://doi.org/10.1007/s10490-023-09871-y
Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), Article 6597. https://doi.org/10.3390/su12166597
Emon, M. M. H., Hassan, F., Nahid, M. H., & Rattanawiboonsom, V. (2023). Predicting adoption intention of artificial intelligence: A study on ChatGPT. AIUB Journal of Science and Engineering, 22(2), 189–196. https://doi.org/10.53799/ajse.v22i2.797
Estrellado, C. J., & Miranda, J. C. (2023). Artificial intelligence in the Philippine educational context: Circumspection and future inquiries. International Journal of Scientific and Research Publications, 13(5), 16–22. https://doi.org/10.29322/IJSRP.13.05.2023.p13704
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 21(1), 37–56. https://doi.org/10.1177/14757257211037149
Gamad, L. C., Khayduangta, M. D., Birdsell, N. N., Prepotente, M. N. A., Sursigis, P. L., Hugo, K. K. G., & Princena, M. A. T. (2025). Global Filipino teachers’ readiness on Education 5.0: Reinforcing the status quo. Review of Integrative Business and Economics Research, 14(2), 519–538.
Giannini, S. (2023, July 3). Generative artificial intelligence and the future of education. UNESCO. https://www.unesco.org/en/articles/generative-artificial-intelligence-and-future-education
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Hu, K. (2023, February 2). ChatGPT sets record for fastest-growing user base. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-2023-02-02/
Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kyambade, M., Namatovu, A., & Ssentumbwe, A. M. (2025). Exploring the evolution of artificial intelligence in education: From AI-guided learning to learner-personalized paradigms. Cogent Education, 12(1), Article 2505297. https://doi.org/10.1080/2331186X.2025.2505297
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2022). Artificial intelligence and education: A critical but hopeful perspective. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000380602
Motlagh, N. Y., Khajavi, M., Sharifi, A., & Ahmadi, M. (2023). The impact of artificial intelligence on the evolution of digital education: A comparative study of OpenAI text generation tools including ChatGPT, Bing Chat, Bard, and Ernie. arXiv. https://doi.org/10.48550/arXiv.2309.02029
Rönkkö, M., & Cho, E. (2022). An updated guideline for assessing discriminant validity. Organizational Research Methods, 25(1), 6–14. https://doi.org/10.1177/1094428120968614
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 25. https://doi.org/10.1186/s41239-024-00448-0
Wang, F., King, R. B., Chai, C. S., & Zhou, Y. (2023). University students’ intentions to learn artificial intelligence: The roles of supportive environments and expectancy–value beliefs. International Journal of Educational Technology in Higher Education, 20, Article 51. https://doi.org/10.1186/s41239-023-00417-2
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zhong, R., & Zhao, Y. (2025). Education paradigm shifts in the age of AI: A spatiotemporal analysis of learning. ECNU Review of Education, 8(2). https://doi.org/10.1177/20965311251315204
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
