Mathematical Resilience and AI-Supported Problem-Solving Readiness Among Secondary School Students

Authors

  • Jesalyn M. Asuncion Northeastern College Author

DOI:

https://doi.org/10.5281/zenodo.19724127

Keywords:

Mathematical resilience, artificial intelligence, problem-solving readiness, secondary school students, mathematics education, educational technology

Abstract

This study addressed the emerging need to understand how students’ capacity to persist in mathematics relates to their readiness to use artificial intelligence as a support for problem solving. It focused on mathematical resilience and AI-supported problem-solving readiness among secondary school students at Isabela National High School in Ilagan, Isabela. Using a quantitative descriptive-correlational design, the study determined the level of students’ mathematical resilience, their level of AI-supported problem-solving readiness, the significant relationship between the two variables, and the dimensions of mathematical resilience that significantly predicted readiness. Data were gathered through a structured survey questionnaire administered to selected secondary school students using stratified random sampling. Weighted mean, standard deviation, Pearson product-moment correlation, and multiple regression analysis were used to analyze the data. Findings revealed that the respondents demonstrated a high level of mathematical resilience and a high level of AI-supported problem-solving readiness. Results further showed a significant positive relationship between the two variables, suggesting that students who were more resilient in mathematics also tended to be more prepared to use AI-supported tools in solving mathematical problems. Regression analysis indicated that confidence in handling mathematics challenges, persistence in solving difficult tasks, and willingness to seek help and use strategies significantly predicted AI-supported problem-solving readiness. The study concluded that mathematical resilience may serve as an important foundation for students’ responsible, critical, and productive engagement with AI in mathematics learning. It recommended that mathematics instruction include resilience-building activities and guided AI use to strengthen students’ independent reasoning, ethical awareness, and problem-solving competence.

Downloads

Download data is not yet available.

References

Akkan, S. N., & Horzum, T. (2024). Illuminating the landscape of mathematical resilience: A systematic review. Journal of Pedagogical Research, 8(1), 312–338. https://doi.org/10.33902/JPR.202420093

Aravantinos, S., Lavidas, K., Tsinakos, A., & Manesis, D. (2026). Artificial intelligence in K-12 education: A systematic review of teachers’ professional development needs for AI integration. Computers, 15(1), Article 49. https://doi.org/10.3390/computers15010049

Department of Education. (2026). Foundational guidelines on artificial intelligence (AI) in basic education (Department Order No. 003, s. 2026). Department of Education.

Hidayati, D. N., & Mahmudi, A. (2025). Mathematical resilience and student problem-solving in mathematics learning: Are there any connections? In C. Kusumawardani et al. (Eds.), Proceedings of the 9th International Conference on Research, Implementation, and Education of Mathematics and Sciences (ICRIEMS 2022) (Advances in Social Science, Education and Humanities Research, Vol. 957). Atlantis Press. https://doi.org/10.2991/978-2-38476-481-5_22224

Joshi, D. R., Khanal, B., Chapai, K. P. S., & Singh, A. B. (2026). Role of secondary school students’ AI acceptance in mathematics learning in shaping academic achievement. Social Sciences & Humanities Open, 13, Article 102235. https://doi.org/10.1016/j.ssaho.2025.102235

Lee, S. J., & Kwon, K. (2024). A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Computers and Education: Artificial Intelligence, 6, Article 100211. https://doi.org/10.1016/j.caeai.2024.100211

Neumann, I., Jeschke, C., & Heinze, A. (2021). First-year students’ resilience to cope with mathematics exercises in the university mathematics studies. Journal für Mathematik-Didaktik, 42, 17–47. https://doi.org/10.1007/s13138-020-00177-w

OECD. (2023). PISA 2022 results: Country note, Philippines. Organisation for Economic Co-operation and Development.

UNESCO. (2025). AI and education: Protecting the rights of learners. United Nations Educational, Scientific and Cultural Organization.

UNESCO. (n.d.). Artificial intelligence in education. United Nations Educational, Scientific and Cultural Organization.

Xenofontos, C., & Mouroutsou, S. (2023). Resilience in mathematics education research: A systematic review of empirical studies. Scandinavian Journal of Educational Research, 67(7), 1041–1055. https://doi.org/10.1080/00313831.2022.2115132

Yarkwah, C., Essuman, S. O., & Oduro, F. T. (2024). Effect of test anxiety on students’ academic performance in mathematics at the senior high school level. Discover Education, 3, Article 343. https://doi.org/10.1007/s44217-024-00343-z

Zhong, B., & Liu, X. (2025). Evaluating AI literacy of secondary students: Framework and scale development. Computers & Education, 227, Article 105230. https://doi.org/10.1016/j.compedu.2024.105230

Downloads

Published

2026-04-24

How to Cite

Asuncion, J. (2026). Mathematical Resilience and AI-Supported Problem-Solving Readiness Among Secondary School Students. International Journal of Education, Research, and Innovation Perspectives, 2(4), 1266-1278. https://doi.org/10.5281/zenodo.19724127

Similar Articles

131-140 of 250

You may also start an advanced similarity search for this article.