AI Literacy and Academic Performance: A Cross-Sectional Analysis of Senior High School Students
DOI:
https://doi.org/10.46328/ijte.5335Keywords:
AI literacy, Artificial intelligence, Academic performance, Student learning, PhilippinesAbstract
This study investigates the relationship between artificial intelligence (AI) literacy and academic performance among senior high school students using the validated Artificial Intelligence Literacy Scale (AILS). A cross-sectional correlational study examined 525 students from four academic strands. Despite widespread AI tool adoption (85.7% use ChatGPT), learning remains predominantly informal and peer-driven rather than teacher-guided. AI literacy was measured across four dimensions: awareness, usage, evaluation, and ethics. Academic performance was assessed through grade point averages and standardized test scores. Results reveal significant positive relationships between AI literacy and academic performance (r = .27-.28, p < .001), with awareness and ethics dimensions emerging as primary predictors over technical usage skills. AI literacy explained 7.2% of variance in grades and 7.7% of variance in test scores. Students demonstrated significant variations across academic strands—STEM students significantly outperformed business and general academic students, while humanities students achieved levels comparable to STEM students. This suggests interdisciplinary approaches combining critical thinking with technology understanding may be optimal for AI literacy development. The prominence of conceptual understanding over technical skills challenges prevailing assumptions about AI education priorities. Findings provide empirical evidence for integrating strand-specific AI literacy curricula and demonstrate urgent need for systematic AI literacy education to address current informal learning gaps in secondary education globally.
References
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173
Almerino, P. M., Ocampo, L. A., Abellana, D. P. M., Almerino, J. G. F., Mamites, I. O., Pinili, L. C., Tenerife, J. J. L., Sitoy, R. E., Abelgas, L. J., & Peteros, E. D. (2020). Evaluating the academic performance of K-12 students in the Philippines: A standardized evaluation approach. Education Research International, 2020, 1-8. https://doi.org/10.1155/2020/8877712
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/s40594-023-00418-7
Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Co, S. (2025). Student evaluation of blended learning implementation and faculty performance in online components: A comparative analysis across senior high school grade levels and academic strands. International Journal on Open and Distance e-Learning, 10(2). https://doi.org/10.58887/ijodel.v10i2.287
Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial Intelligence Literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291-306. https://doi.org/10.52911/itall.1401740
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025).
Challenging Cognitive Load Theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203
Gorospe, J. D., & Joaquin, J. M. (2022). On the right track: Does senior high school tracking matter? Technium Social Sciences Journal, 31, 174-182. https://doi.org/10.47577/tssj.v31i1.6434
Gouseti, A., James, F., Fallin, L., & Burden, K. (2024). The ethics of using AI in K-12 education: A systematic literature review. Technology Pedagogy and Education, 1-22. https://doi.org/10.1080/1475939x.2024.2428601
Gu, X., & Ericson, B. J. (2024). AI literacy in K-12 and higher education in the wake of generative AI: An integrative review. ACM Transactions on Computing Education, 24(2), Article 29. https://doi.org/10.1145/3657475
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education Artificial Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165
Jadaone, G. Y., Jadaone, G. Y., Quintana, K. A., Makabenta, R. J. P., & Leona, A. A. (2022). Collective experiences and perceptions of senior high school students in blended learning. International Journal of Research Publications, 103(1), 482-505. https://doi.org/10.47119/ijrp1001031620223403
Lei, H., Xiong, Y., Chiu, M. M., Zhang, J., & Cai, Z. (2021). The relationship between ICT literacy and academic achievement among students: A meta-analysis. Children and Youth Services Review, 127, 106123. https://doi.org/10.1016/j.childyouth.2021.106123
Li, F., Cheng, L., Wang, X., Shen, L., Ma, Y., & Islam, A. Y. M. A. (2025). The causal relationship between digital literacy and students' academic achievement: A meta-analysis. Humanities and Social Sciences Communications, 12, 108. https://doi.org/10.1057/s41599-025-04399-6
Maria, M., Baber, S., & Alam, I. (2024). Effects of Digital Literacy on Students’ Academic Performance at Secondary Level. Pakistan Languages and Humanities Review, 8(3), 515–532. Retrieved from https://ojs.plhr.org.pk/journal/article/view/1053
Masdoki, M., Din, R., & Matore, E. M. (2024). Towards Educator 4.0: Technology competency-based teaching. International Conference on Business Studies and Education, 173-181.
Mehrvarz, M., Heidari, E., Farrokhnia, M., & Noroozi, O. (2021). The mediating role of digital informal learning in the relationship between students' digital competence and their academic performance. Computers & Education, 167, 104184. https://doi.org/10.1016/j.compedu.2021.104184
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Sopandi, E., Anwar, S., Habibah, N., Suhana, Manurung, S., Alia, N., & Hendrawati, S. (2022). The role of digital literacy and its relation with performance of Madrasah Aliyah students. Eurasian Journal of Educational Research, 104, 16-32. https://doi.org/10.14689/ejer.2023.104.002
Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6, 100159. https://doi.org/10.1016/j.caeo.2024.100159
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education Artificial Intelligence, 4, 100124. https://doi.org/10.1016/j.caeai.2023.100124
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4
The jamovi Project. (2022). jamovi (Version 2.3) [Computer software]. https://www.jamovi.org
Uğraş, H., Doğan, M., & Uğraş, M. (2024). Adaptation of Artificial Intelligence Literacy Scale into Turkish: A sample of pre-service teachers. E-Kafkas Journal of Educational Research, 11(4), 688-701. https://doi.org/10.30900/kafkasegt.1429630
Vasilaki, E., & Mavrogianni, A. (2025). Extending Cognitive Load Theory: The CLAM framework for biometric, adaptive, and ethical learning. Psychology International, 7(2), 40. https://doi.org/10.3390/psycholint7020040
Wang, B., Rau, P. L. P., & Yuan, T. (2022). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324-1337. https://doi.org/10.1080/0144929X.2022.2072768
Zhou, X., Li, Y., Chai, C. S., & Chiu, T. K. F. (2025). Defining, enhancing, and assessing artificial intelligence literacy and competency in K-12 education from a systematic review. Interactive Learning Environments, 1-23. https://doi.org/10.1080/10494820.2025.2487538
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