Artificial Intelligence on the Learning Performance of Senior High School Students

This study examined the use of artificial intelligence (AI)-based learning tools and their relationship to the learning performance of Senior High School students at Kibatang National High School. Specifically, it aimed to determine the types of AI-based learning tools used by students, assess the level of AI utilization in terms of frequency of use and learning purposes, identify the students' level of learning performance, and determine whether there is a significant relationship between the use of AI-based learning tools and learning performance. The study employed a quantitative descriptive-correlational research design. A total of 100 Senior High School students, consisting of 50 Grade 11 and 50 Grade 12 students, participated in the study. Data were collected through a survey questionnaire, while the students' General Weighted Average (GWA) was used to determine their learning performance. The gathered data were analyzed using appropriate statistical tools at the 0.05 level of significance.
The findings revealed that ChatGPT was the most commonly used AI-based learning tool, followed by Google Gemini, QuillBot, and Brainly, while Grammarly, Perplexity AI, and Quizlet were moderately used. The level of AI utilization in terms of frequency of use obtained a mean score of 3.79 with a descriptive rating of Agree, while the level of use based on learning purposes obtained a mean score of 3.83, also interpreted as Agree, indicating a high level of AI utilization among the respondents. In terms of learning performance, most students obtained a General Weighted Average (GWA) ranging from 88 to 92. However, the results further revealed that there was no significant relationship between the use of AI-based learning tools and the learning performance of Senior High School students. The study concluded that students prefer AI applications that provide immediate, accessible, and versatile academic support across various learning tasks and subject areas.

Artificial Intelligence, AI-Based Learning Tools, Learning Performance, Senior High School Students, Academic Achievement

1. Abao-an, D. D. (2026). Level of vocabulary knowledge depth and reading comprehension of Senior High School: Basis for reading intervention program. Zenodo. https://doi.org/10.5281/zenodo.18125970 [Google Scholar] [Crossref]

2. Afidchao, D. T., Bigayan, J. J. C., Galindez, M. S. S. M., Jimenez, E. J. B., Macam, F. K. C., Orias, C. E. L., Balonquita, M. C., & Bernardino, R. M. (2023). Perceived effectiveness of artificial intelligence-powered calculators on the academic performance of senior high school STEM students in mathematics [Dataset/report]. Zenodo. https://doi.org/10.5281/zenodo.8104870 [Google Scholar] [Crossref]

3. Agbong-Coates, I. J. G. (2024). ChatGPT integration significantly boosts personalized learning outcomes: A Philippine study. International Journal of Educational Management and Development Studies, 5(2), 165–186. https://doi.org/10.53378/353067 [Google Scholar] [Crossref]

4. AlAfnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2024). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for higher education. Journal of Applied Learning and Teaching, 7(1), 1–14. https://doi.org/10.37074/jalt.2024.7.1.6 [Google Scholar] [Crossref]

5. Baidoo-Anu, D., & Owusu Ansah, L. (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 [Google Scholar] [Crossref]

6. Carroza-Pacheco, A. M., León-del-Barco, B., & Bringas Molleda, C. (2025). Academic performance and resilience in secondary education students. Journal of Intelligence, 13(5), 56. https://doi.org/10.3390/jintelligence13050056 [Google Scholar] [Crossref]

7. Cayetano, N. A. V., De Dios, F. C., Francisco, L. M., Hernandez, I. Z. N., Sequito, F. L. V., Sevilla, S. M. C., & Sison, L. B. (2025). The effect of ChatGPT 3.0 as a personalized learning tool in answering atomic structure assessments. American Journal of Education and Technology, 4(3), 1–11. https://doi.org/10.54536/ajet.v4i3.4700 [Google Scholar] [Crossref]

8. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges of ChatGPT in higher education. Educational Technology Research and Development, 71(6), 3145–3165. https://doi.org/10.1007/s11423-023-10296-0 [Google Scholar] [Crossref]

9. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 [Google Scholar] [Crossref]

10. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]

11. https://doi.org/10.2307/249008 [Google Scholar] [Crossref]

12. De Guzman, A. B. (2024). Factors affecting the academic performance of Senior High School students in public secondary schools in the Philippines. Philippine Journal of Education, 98(2), 45–59. [Google Scholar] [Crossref]

13. Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2024). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224 [Google Scholar] [Crossref]

14. Dong, L., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers & Education: Artificial Intelligence, 8, 100400. https://doi.org/10.1016/j.caeai.2025.100400 [Google Scholar] [Crossref]

15. du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), Article e39630. https://doi.org/10.1016/j.heliyon.2024.e39630 [Google Scholar] [Crossref]

16. Espartinez, A. S. (2024). Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study. Computers and Education: Artificial Intelligence, 7, Article 100264. https://doi.org/10.1016/j.caeai.2024.100264 [Google Scholar] [Crossref]

17. Fabro, R. B. B. (2024). Perceptions and extent of utilization of generative artificial intelligence among Filipino students. International Journal of Education and Research, 12(7), 119–128. https://ijern.com/journal/2024/July-2024/09.pdf [Google Scholar] [Crossref]

18. Fütterer, T., Fischer, C., Alekseeva, A., Chen, X., Tate, T., Warschauer, M., et al. (2023). ChatGPT in education: Global reactions to AI innovations. Scientific Reports, 13, Article 15310. https://doi.org/10.1038/s41598-023-42227-6 [Google Scholar] [Crossref]

19. García-Martínez, I., Fernández-Batanero, J. M., Cobos-Sanchiz, D., & Luque-de la Rosa, A. (2024). Digital technologies and academic performance in secondary education: Evidence from recent educational research. Education and Information Technologies, 29, 5673–5692. https://doi.org/10.1007/s10639-023-12011-4 [Google Scholar] [Crossref]

20. Hattie, J. (2023). Visible learning: The sequel – A synthesis of over 2,100 meta-analyses relating to achievement. Routledge. [Google Scholar] [Crossref]

21. Jalon, J. B., Jr. (2024). ChatGPT as a learning assistant: Its impact on students’ learning of Python programming. ERIC / Education Resources Information Center (EJ1465724). https://files.eric.ed.gov/fulltext/EJ1465724.pdf [Google Scholar] [Crossref]

22. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerger, M., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 [Google Scholar] [Crossref]

23. Kassim, M. A., Marfo, S., & Abu, K. (2024). Assessing the impact of five teaching strategies on the academic performance of senior high school students in financial accounting: A case study in Wa. Social Sciences & Humanities Open, 10, Article 101259. https://doi.org/10.1016/j.ssaho.2024.101259 [Google Scholar] [Crossref]

24. Kim, H., & Kim, M. (2023). Factors influencing academic achievement among secondary school students. Educational Sciences, 13(4), 358. https://doi.org/10.3390/educsci13040358 [Google Scholar] [Crossref]

25. Lo, C. K., Hew, K. F., & Jong, M. S. Y. (2024). The influence of ChatGPT on student engagement: A systematic review and future research agenda. Computers & Education, 219, Article 105100. https://doi.org/10.1016/j.compedu.2024.105100 [Google Scholar] [Crossref]

26. Mensah, R. O., Amponsah, K. D., Adiza Babah, P., & Jibril, H. S. (2024). Factors affecting students’ academic performance and teachers’ efficiency in Ghana: A case study of Wa Senior High School. Cogent Arts & Humanities, 11(1), Article 2412944. https://doi.org/10.1080/23311983.2024.2412944 [Google Scholar] [Crossref]

27. Muktar, R. (2025). Impact of artificial intelligence applications on mathematics learning among Grade 11 STEM students at Mindanao State University-Sulu. (Precalculus study). Zenodo. https://doi.org/10.5281/zenodo.15608460 [Google Scholar] [Crossref]

28. Pattung, A. G. (2025). Use of generative AI tools among Senior High School Grade 12 students: Inputs for policy recommendations. ISRG Journal / conference paper (full text). https://isrgpublishers.com/wp-content/uploads/2026/01/ISRGJAHSS1003602025.pdf [Google Scholar] [Crossref]

29. Quirino State University research team. (2024). Unlocking the future of learning: Assessing students’ awareness and usage of AI tools. International Journal of Information and Education Technology, 14(8), 1137–1145. https://www.ijiet.org/vol14/IJIET-V14N8-2142.pdf [Google Scholar] [Crossref]

30. Ravšelj, D., Umek, L., Brezovar, N., Keržič, D., & Aristovnik, A. (2024). Higher education students’ perceptions of ChatGPT: A global study of early reactions. (Open access; global survey article referenced on PMC). (Full text) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798494/ [Google Scholar] [Crossref]

31. Rujonel F. (2024). The impact of classroom decors on academic performance and involvement of Senior High School students in Central Visayas, Philippines. Zenodo. https://doi.org/10.5281/zenodo.14272776 [Google Scholar] [Crossref]

32. Santos, R. M., & Mendoza, L. P. (2025). Digital learning platforms and academic performance of Grade 11 students in public secondary schools. Asia Pacific Journal of Educational Research, 8(1), 112–126. [Google Scholar] [Crossref]

33. Schiel, J. (2023). High-school students' use and impressions of AI tools (report). Lumina Foundation. https://www.luminafoundation.org/wp-content/uploads/2024/01/High-School-Students-Use-and-Impressions-of-AI-Tools.pdf [Google Scholar] [Crossref]

34. Sweller, J. (1998). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 [Google Scholar] [Crossref]

35. Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., et al. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215, Article 105031. https://doi.org/10.1016/j.compedu.2024.105031 [Google Scholar] [Crossref]

36. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]

37. Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12, Article 621. https://doi.org/10.1038/s41599-025-04787-y [Google Scholar] [Crossref]

38. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167 [Google Scholar] [Crossref]

39. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.https://doi.org/10.1207/s15430421tip4102_2 [Google Scholar] [Crossref]

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