Home⇛DAVAO RESEARCH JOURNAL⇛vol. 16 no. 4 (2025)

Artificial Intelligence (AI) Utilization as a Mediator Between Students’ Study Attitude and Mathematics Achievement

Lysander Roquero | Rain Jhon Rollon | Gemar Magbutong

Discipline: Education

 

Abstract:

Artificial Intelligence (AI) integration in education has sparked interest in its effect on student learning. This study examined AI utilization as a mediator in the relationship between students’ study attitude and mathematics achievement, involving 323 BSED Mathematics students across four campuses of Davao de Oro State College, of whom 195 were identified as AI users. A quantitative, descriptive-correlational design was employed, with data collected through stratified random sampling. An adapted questionnaire measured study attitude (17 items) and AI utilization (25 items), while mathematics grades from the first semester of the academic year 2024–2025 served as indicators of academic performance. Statistical analyses included mean, standard deviation, Pearson correlation, and Sobel’s Test. Findings revealed that students generally demonstrated high levels of study attitude, whereas AI utilization was moderate. Mathematics achievement was classified as very satisfactory. Despite these positive indicators, the relationship between AI utilization and mathematics achievement was weak and non-significant, and mediation analysis showed no significant mediating effect of AI utilization. In conclusion, the study indicates that although students exhibited strong study attitudes and satisfactory mathematics performance, AI utilization did not significantly influence achievement or mediate the relationship between study attitude and performance. This suggests that students’ learning attitudes remain more influential than AI use, while AI tools function primarily as supplementary support. Strengthening students’ study habits and attitudes should remain a priority in enhancing learning outcomes. Further research is recommended to determine the conditions under which AI may more effectively contribute to academic performance and inform broader educational strategies.



References:

  1. Abulela, M. A. A., & Harwell, M. M. (2020). Data analysis: Strengthening inferences in quantitative education studies conducted by novice researchers. Kuram Ve Uygulamada Egitim Bilimleri, 20(1), 59–78. https://doi.org/10.12738/JESTP.2020.1.005
  2. Afidchao, D. T., Bigayan, J. J. C. 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. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.8104870
  3. Alim, A., & Shukla, D. (2019). An application approach of stratified sampling in analytic-predictive environments of big data. Social Science Research Network. https://doi.org/10.2139/SSRN.3356445
  4. Amiruzzaman, M. (2020). Exploring students’ understanding of statistical mean. Preprints. https://doi.org/10.20944/preprints202004.0455.v1
  5. An, D., & Ma, C. (2023). A model of factors influencing learning outcomes based on artificial intelligence: Perspectives of Chinese university students. Journal of Logistics, Informatics and Service Science, 11(1). https://doi.org/10.33168/jliss.2024.0126
  6. Bacong, J. T., Encabo, C. M. T., Limana, J. M. B., & Cabello, C. A. (2023). The high school students’ struggles and challenges in mathematics: A qualitative inquiry. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.8251107
  7. Bernardo, A. B. I., Cordel, M. O., II, Lapinid, M. R. C., Teves, J. M. M., Yap, S. A., & Chua, U. C. (2022). Contrasting profiles of low-performing mathematics students in public and private schools in the Philippines: Insights from machine learning. Journal of Intelligence, 10(3), 61. https://doi.org/10.3390/jintelligence10030061
  8. Cabilan, J. B., & Peteros, E. D. (2024). Predictive analysis of independent learning bearing on students’ mathematics performance in Davao de Oro, Philippines. Journal on Mathematics Education, 15(4), 1409–1432. https://doi.org/10.22342/jme.v15i4.pp1409-1432
  9. Calkins, D. S. (2017). Some effects of non-normal distribution shape on the magnitude of the Pearson moment correlation coefficient. Interamerican Journal of Psychology, 8. https://psycnet.apa.org/record/1975-26610-001
  10. Callaman, R. A., & Itaas, E. C. (2020). Students’ mathematics achievement in the Mindanao context: A meta-analysis. Journal of Research and Advances in Mathematics Education, 5(2), 148–159. https://doi.org/10.23917/JRAMATHEDU.V5I2.10282
  11. Caratiquit, K. D., & Caratiquit, L. J. (2023). ChatGPT as an academic support tool on the academic performance among students: The mediating role of learning motivation. Journal of Social, Humanity, and Education. https://doi.org/10.35912/jshe.v4i1.1558
  12. Chand, S., Chaudhary, K., Prasad, A., & Chand, V. (2021). Perceived causes of students’ poor performance in mathematics: A case study at BA and Tavua Secondary Schools. Frontiers in Applied Mathematics and Statistics, 7. https://doi.org/10.3389/fams.2021.614408
  13. Eberly, L. E. (2007). Correlation and simple linear regression. Methods in Molecular Biology, 143–164. https://doi.org/10.1007/978-1-59745-530-5_8
  14. EdTrust. (2024). Breaking down the nation’s math scores - EdTrust. https://edtrust.org/blog/breaking-down-the-nations-math-scores/
  15. Efendi, M., Panglipur, I. R., & Murtinasari, F. (2024). Identifying the use of artificial intelligence in math learning based on learning outcomes. At-Ta‘lim: Jurnal Pendidikan, 10(2), 53–59. https://doi.org/10.55210/attalim.v10i2.1689
  16. Emata, C. Y. (2023). The moderating effect of technology attitude on the relationship between math self-efficacy and attitudes towards mathematics. Unnes Journal of Mathematics Education, 12(1), 1–12. https://doi.org/10.15294/ujme.v12i1.62791
  17. Fitriya, Y., Mustadi, A., Nugroho, I. A., & Anugrahana, A. (2024). Students’ mathematical disposition and resilience: An analysis of student perceptions in numbers and algebra courses. Journal of Electrical Systems. https://doi.org/10.52783/jes.2479
  18. Galangco, J. (2023). Path model of mathematics achievement in senior high school. Journal of Research in Mathematics Education, 12(3), 246–264. https://doi.org/10.17583/redimat.12759
  19. Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2023). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic application index in higher education. Cogent Education, 11(1). https://doi.org/10.1080/2331186x.2023.2287917
  20. Gupta, N. N. K. S. (2025). Relationship between attitude towards mathematics and academic achievement of eleventh-class students. Journal of Informatics Education and Research, 5(1). https://doi.org/10.52783/jier.v5i1.2074
  21. Hwang, S., & Son, T. (2021). Students’ attitude toward mathematics and its relationship with mathematics achievement. Journal of Education and eLearning Research, 8(3), 272–280. https://doi.org/10.20448/journal.509.2021.83.272.280
  22. Kelly, R. (2024). Survey: 86% of students already use AI in their studies. https://campustechnology.com/Articles/2024/08/28/Survey-86-of-Students-Already-Use-AI-in-Their-Studies.aspx
  23. Körpeoğlu, S., & Yıldız, S. (2023). Using artificial intelligence to predict students’ STEM attitudes: An adaptive neural-network-based fuzzy logic model. International Journal of Science Education, 1–26. https://doi.org/10.1080/09500693.2023.2269291
  24. Macaskill, P. (2018). Standard deviation and standard error: Interpretation, usage, and reporting. The Medical Journal of Australia, 208(2), 63–64. https://doi.org/10.5694/MJA17.00633
  25. Magnello, M. (2005). Karl Pearson’s paper on the chi-square goodness of fit test (1900). In Elsevier eBooks (pp. 724–731). https://doi.org/10.1016/b978-044450871-3/50137-6
  26. Mallillin, L. L. D. (2024). Artificial intelligence (AI) towards students’ academic performance. Innovare Journal of Education, 16–21. https://doi.org/10.22159/ijoe.2024v12i4
  27. Maulida, L., Nurossobah, P., Aura, B. A., Nengsih, E. D., & Rasilah, R. (2024). Improving the effectiveness of mathematics learning through artificial intelligence: Literature review. Journal of General Education and Humanities, 3(4), 323–338. https://doi.org/10.58421/gehu.v3i4.267
  28. Mazana, M. Y., Montero, C. S., & Casmir, R. O. (2018). Investigating students’ attitude towards learning mathematics. International Electronic Journal of Mathematics Education, 14(1). https://doi.org/10.29333/iejme/3997
  29. Mazana, M. Y., Montero, C. S., & Casmir, R. O. (2020). Assessing students’ performance in mathematics in Tanzania: The teacher’s perspective. International Electronic Journal of Mathematics Education, 15(3), em0589. https://doi.org/10.29333/iejme/7994
  30. Murniati, M., & Erika, E. (2023). Attitudes of students: Adoption of scientific attitudes and interest in increasing study. SJPE, 4(1), 7–11. https://doi.org/10.37251/sjpe.v4i1.491
  31. Mutiawati, M., Mailizar, M., & Johar, R., Ramli, M. (2023). Exploration of factors affecting changes in student learning behavior: A systematic literature review. International Journal of Evaluation and Research in Education (IJERE), 12, 1315. https://doi.org/10.11591/ijere.v12i3.24601
  32. Nicolas, C. A. T., & Emata, C. Y. (2018). An integrative approach through reading comprehension to enhance problem-solving skills of Grade 7 mathematics students. International Journal of Innovation in Science and Mathematics Education, 26(3). https://openjournals.library.sydney.edu.au/index.php/CAL/article/download/12497/11671
  33. Nolasco, D. (2025). Unearthing mathematics anxiety: A qualitative exploration of student experiences. Journal of Technology and Science Education, 15(2), 223–239. https://doi.org/10.3926/jotse.2950
  34. Norouzi, H. (2023). Predicting academic self-efficacy based on self-regulation and academic emotions in high school students in Shiraz. JSIED, 3(4), 31–41. https://doi.org/10.61838/jsied.3.4.4
  35. OECD. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD. https://doi.org/10.1787/53f23881-en
  36. Oluwadayo, A. T. (2024). The predictive effects of teaching methods on students’ achievement in mathematics. Sapienza Foundation Journal of Education, Sciences and Gender Studies. http://www.sfjesgs.com/index.php/SFJESGS/article/view/495
  37. Omarov, B., Omarov, B., Rakhymzhanov, A., Niyazov, A., Sultan, D., & Baikuvekov, M. (2024). Development of an artificial intelligence-enabled non-invasive digital stethoscope for monitoring the heart condition of athletes in real-time. Retos, 60, 1169–1180. https://doi.org/10.47197/retos.v60.108633
  38. Peteros, E., Gamboa, A., Etcuban, J. O., Dinauanao, A., Sitoy, R., & Arcadio, R. (2019). Factors affecting mathematics performance of junior high school students. International Electronic Journal of Mathematics Education, 15(1), 1–13. https://doi.org/10.29333/iejme/5938
  39. Pratama, R., Aisyah, S. A., Putra, A. M., Sirodj, R. A., & Afgan, M. W. (2023). Correlational research. JIIP (Jurnal Ilmiah Ilmu Pendidikan), 6(3), 1754–1759. https://doi.org/10.54371/jiip.v6i3.1420
  40. Sasikala, P., & Ravichandran, R. (2024). Study on the impact of artificial intelligence on student learning outcomes. Journal of Digital Learning and Education, 4(2), 145–155. https://doi.org/10.52562/jdle.v4i2.1234
  41. Shi, Y., Cao, C., Chen, H., Qu, Z., Duan, J., & Yang, H. H. (2024). Study the influencing factors of junior high school students’ cognitive load in the smart classroom environment. 2024 International Symposium on Educational Technology (ISET), 347–351. https://doi.org/10.1109/iset61814.2024.00075
  42. Velez, A. J. B., Dayaganon, D. G. F., Robigid, J. C., Demorito, J. D., Villegas, J. P., & Gomez, D. O. (2023). Difficulties and coping strategies in understanding mathematical concepts in a private higher education in Tagum City, Davao del Norte, Philippines. Davao Research Journal (DRJ), 14(1), 45–54. https://doi.org/10.59120/drj.v14i1.10
  43. Wang, L., & Luo, J. (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: Serial mediating roles of self-efficacy and cognitive engagement. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2023.1285392
  44. Wu, L. (2024). AI-based writing tools: Empowering students to achieve writing success. Advances in Educational Technology and Psychology, 8(2). https://doi.org/10.23977/aetp.2024.080206
  45. Yáñez-Marquina, L., & Villardón-Gallego, L. (2016). Attitudes towards secondary-level mathematics: Development and structural validation of the Scale for Assessing Attitudes towards Mathematics in Secondary Education (SATMAS). Electronic Journal of Research in Educational Psychology, 14(3), 557–581. https://doi.org/10.14204/ejrep.40.15163
  46. Yuan, B., & Hu, J. (2024). Generative AI as a tool for enhancing reflective learning in students. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.02603
  47. Zamora, J. E., Edig, M. M. N., & Decano, R. S. (2022). Math-tulungan para math-tuto: A technology-based tutorial as intervention in solving problems involving probability. EPRA International Journal of Environmental Economics, Commerce and Educational Management, 26–30. https://doi.org/10.36713/epra9925.
  48. Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J., Yuan, J., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, 1–18. https://doi.org/10.1155/2021/8812542

Tools


Copyright © 2026  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic