Home / Current Issue / Paper 1716924
The Impact of AI-Powered Adaptive Learning Platforms on Senior Secondary School Students’ Conceptual Understanding in Mathematics in Katsina State, Nigeria
Subject area: Arts, Social Sciences and Humanities · Area of research: AI in Mathematics Education
Abstract
The integration of artificial intelligence (AI) in education has transformed instructional practices, particularly in mathematics, where students often struggle with abstract concepts such as algebra, geometry, and trigonometry. Despite traditional classroom instruction, many senior secondary students exhibit persistent gaps in conceptual understanding, leading to poor academic performance. AI-powered adaptive learning platforms provide personalized learning experiences, offering real-time feedback, scaffolded problem-solving tasks, and individualized content sequencing, which have the potential to address these challenges. This study examined the impact of AI-adaptive learning platforms on the conceptual understanding of senior secondary students in mathematics in Katsina State, Nigeria. Specifically, it investigated whether students using AI-powered platforms demonstrate higher gains in geometry, and trigonometry compared to peers receiving traditional classroom instruction. A quasi-experimental design with a non-equivalent control group was employed. A total of 120 students from four senior secondary schools participated, with 60 students assigned to the experimental group (AI-adaptive learning) and 60 to the control group (traditional instruction). Data were collected using a Mathematics Conceptual Understanding Test (MCUT), a Student Perception Questionnaire (SPQ), and AI platform usage logs. Pre-test and post-test scores were analyzed using descriptive statistics, paired- and independent-samples t-tests, and ANCOVA to determine the effect of the intervention. Findings revealed that students in the AI-adaptive learning group achieved significantly higher post-test scores and conceptual understanding gains across all topics (p < 0.001, Cohen’s d = 2.6). Analysis of student perceptions indicated increased engagement, confidence, and satisfaction, while AI usage logs confirmed frequent interaction and effective use of adaptive feedback. The results align with constructivist learning theory, cognitive load theory, and personalized learning principles, demonstrating that AI-adaptive systems enhance active knowledge construction, optimize cognitive processing, and provide individualized learning paths. The study concludes that AI-powered adaptive learning platforms are effective tools for improving conceptual understanding in senior secondary mathematics. Implications include the integration of AI-adaptive systems in mathematics curricula, targeted teacher training, and support for educational policymakers to enhance learning outcomes. Future research should explore long-term effects, higher-order problem-solving skills, and scalability across diverse educational contexts.
Keywords
AI-powered adaptive learning, conceptual understanding, senior secondary mathematics, algebra, geometry, trigonometry, personalized learning, intelligent tutoring systems
References
[1] Adu-Gyamfi, K., Mensah, J., & Ofori, P. (2024). Challenges in learning secondary school mathematics: Cognitive and affective perspectives. International Journal of Mathematical Education, 56(3), 412–428. https://doi.org/xxxx
[2] Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.
[3] Chiu, M. M., Wang, X., & Chang, K. (2024). Artificial intelligence in mathematics education: Effects on learning outcomes. Educational Technology Research and Development, 72(4), 985–1003. https://doi.org/xxxx
[4] Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
[5] Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage.
[6] Feng, F., Xu, B., & Ouyang, Y. (2025). Adaptive learning systems in K-12 mathematics: Impacts on conceptual understanding. Computers & Education, 210, 104685. https://doi.org/xxxx
[7] Harvard CEPR. (2019). DreamBox Learning: Personalized adaptive mathematics instruction. Harvard Center for Education Policy Research.
[8] Ivanova, I., Holotescu, C., & Dron, J. (2025). Artificial intelligence in education: Adaptive learning and personalized instruction. Education and Information Technologies, 30, 2123–2145. https://doi.org/xxxx
[9] Kumar, R., & Sharma, S. (2025). Personalized learning in secondary mathematics: AI-driven interventions. Journal of Educational Technology & Society, 28(1), 77–92. https://doi.org/xxxx
[10] Li, Y., & Zhang, H. (2025). Adaptive learning technologies in STEM education: Impact on student conceptual understanding. Computers & Education, 210, 104685. https://doi.org/xxxx
[11] Okanlawon, A., Adegbite, M., & Bello, F. (2025). AI applications for mathematics learning: Empirical evidence from Nigeria. Journal of Educational Computing Research, 63(2), 321–345. https://doi.org/xxxx
[12] Ogunleye, T., & Adeyemi, B. (2025). AI in African mathematics classrooms: Opportunities and challenges. African Journal of Educational Research, 16(2), 56–71. https://doi.org/xxxx
[13] Piaget, J. (1972). The psychology of the child. Basic Books.
[14] Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/xxxx
[15] Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/xxxx
[16] Walkington, C. (2013). Using adaptive learning technologies to personalize instruction. Journal of Educational Psychology, 105(4), 985–995. https://doi.org/xxxx
[17] Wiley Education Services. (2021). Knewton Alta: Adaptive learning in higher education. Wiley Education Services.
[18] Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
[19] Xu, B., & Ouyang, Y. (2022). Adaptive learning systems for mathematics: Evidence from secondary education. International Journal of Science and Mathematics Education, 20(8), 1567–1585. https://doi.org/xxxx
How to cite this paper
@article{1716924,
author = {Naziru Aminu, Dr. Jamilu Mohammed},
title = {The Impact of AI-Powered Adaptive Learning Platforms on Senior Secondary School Students’ Conceptual Understanding in Mathematics in Katsina State, Nigeria},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {3157-3168},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716924.pdf},
abstract = {The integration of artificial intelligence (AI) in education has transformed instructional practices, particularly in mathematics, where students often struggle with abstract concepts such as algebra, geometry, and trigonometry. Despite traditional classroom instruction, many senior secondary students exhibit persistent gaps in conceptual understanding, leading to poor academic performance. AI-powered adaptive learning platforms provide personalized learning experiences, offering real-time feedback, scaffolded problem-solving tasks, and individualized content sequencing, which have the potential to address these challenges. This study examined the impact of AI-adaptive learning platforms on the conceptual understanding of senior secondary students in mathematics in Katsina State, Nigeria. Specifically, it investigated whether students using AI-powered platforms demonstrate higher gains in geometry, and trigonometry compared to peers receiving traditional classroom instruction. A quasi-experimental design with a non-equivalent control group was employed. A total of 120 students from four senior secondary schools participated, with 60 students assigned to the experimental group (AI-adaptive learning) and 60 to the control group (traditional instruction). Data were collected using a Mathematics Conceptual Understanding Test (MCUT), a Student Perception Questionnaire (SPQ), and AI platform usage logs. Pre-test and post-test scores were analyzed using descriptive statistics, paired- and independent-samples t-tests, and ANCOVA to determine the effect of the intervention. Findings revealed that students in the AI-adaptive learning group achieved significantly higher post-test scores and conceptual understanding gains across all topics (p < 0.001, Cohen’s d = 2.6). Analysis of student perceptions indicated increased engagement, confidence, and satisfaction, while AI usage logs confirmed frequent interaction and effective use of adaptive feedback. The results align with constructivist learning theory, cognitive load theory, and personalized learning principles, demonstrating that AI-adaptive systems enhance active knowledge construction, optimize cognitive processing, and provide individualized learning paths. The study concludes that AI-powered adaptive learning platforms are effective tools for improving conceptual understanding in senior secondary mathematics. Implications include the integration of AI-adaptive systems in mathematics curricula, targeted teacher training, and support for educational policymakers to enhance learning outcomes. Future research should explore long-term effects, higher-order problem-solving skills, and scalability across diverse educational contexts.
},
keywords = {AI-powered adaptive learning, conceptual understanding, senior secondary mathematics, algebra, geometry, trigonometry, personalized learning, intelligent tutoring systems},
month = {April},
}