Adaptive learning systems in mathematics education: An integrated bibliometric mapping and systematic literature review
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Abstract
Research on adaptive learning systems (ALS) in mathematics education has increased substantially in recent years; however, existing studies remain fragmented across system types, educational levels, and implementation contexts. Most prior reviews have focused either on technological characteristics or learning outcomes, providing limited insight into how adaptive learning systems are systematically implemented and synthesized within mathematics education. Addressing this gap, this study examines adaptive learning systems in mathematics education through an integrated bibliometric mapping and systematic literature review. A systematic literature review was conducted and reported in accordance with PRISMA guidelines to identify, screen, and select relevant studies from major academic databases. Bibliometric mapping analysis using VOSviewer was then employed to explore publication trends, keyword co-occurrence, and thematic structures within the selected literature. The findings indicate that adaptive learning systems generally contribute positively to mathematics learning outcomes; however, their effectiveness is closely associated with pedagogical integration and teacher preparedness rather than technological sophistication alone. A phased implementation pattern is observed, beginning with teacher-led enhancement approaches and progressing toward more technology-mediated models. While AI-based systems demonstrate strong potential, moderately complex adaptive systems appear more feasible for institutions with limited resources. These findings highlight the importance of pedagogical readiness in the adoption of adaptive learning systems and suggest directions for future research, particularly in underrepresented educational levels.
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Nurhayati, S., Sudirman, S., Kartono, K., & Rodríguez-Nieto, C. A. (2026). Adaptive learning systems in mathematics education: An integrated bibliometric mapping and systematic literature review. Kalamatika: Jurnal Pendidikan Matematika, 11(1), 20-46. https://doi.org/10.22236/KALAMATIKA.vol11no1.2026pp20-46
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References
Albar, M. (2025). Challenges in Implementing the Discovery Flipped Classroom Model Assisted by Google Sites in Maths Learning. Pakistan Journal of Life and Social Sciences (PJLSS), 23(1). https://doi.org/10.57239/pjlss-2025-23.1.00392
Anand, U., Biswas, D., & Sardana, S. (2026). Adaptive Learning Systems for Education in Remote and Underserved Regions. In Adaptive Technologies for Sustainable Growth (pp. 504-507). CRC Press. https://doi.org/10.1201/9781003739937
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224–237. https://doi.org/10.1037/0096-3445.130.2.224
Bishop, M. J., Boling, E., Elen, J., & Svihla, V. (2020). Handbook of Research in Educational Communications and Technology: Learning Design: Fifth Edition. In Handbook of Research in Educational Communications and Technology: Learning Design: Fifth Edition. Springer International Publishing. https://doi.org/10.1007/978-3-030-36119-8
Boaler, J. (2016). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages and innovative teaching. Jossey-Bass.
Canonigo, A. M. (2024). Levering AI to enhance students’ conceptual understanding and confidence in mathematics. Journal of Computer Assisted Learning, 40(6), 3215–3229. https://doi.org/10.1111/jcal.13065
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Clark, R., Evans, P., & Moore, S. (2024). Implementing adaptive learning technologies: Practical strategies for mathematics education. Educational Technology Research and Development, 72(3), 456–478.
Egara, F. O., & Mosimege, M. (2024). Effect of flipped classroom learning approach on mathematics achievement and interest among secondary school students. Education and Information Technologies, 29(7), 8131–8150. https://doi.org/10.1007/s10639-023-12145-1
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Homan, K., Marino, M., Vasquez, T., Taub, M., Hunt, J., & Tazi, Y. (2025). Artificial Intelligence Interventions in Mathematics Education: A Systematic Literature Review. Insights into Learning Disabilities, 22(1), 65–91. Retrieved from https://www.ldw-ild.org/images/Holman_et_alpdf.pdf
Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers and Education, 56(4), 1023–1031. https://doi.org/10.1016/j.compedu.2010.12.002
Kilpatrick, J., Swafford, J., & Findell, B. (2001). Adding it up: Helping children learn mathematics. National Academy Press.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. University of Durham.
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420
Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers and Education, 191. https://doi.org/10.1016/j.compedu.2022.104646
Lim, L., Lim, S. H., & Lim, W. Y. R. (2022). A Rasch Analysis of Students’ Academic Motivation toward Mathematics in an Adaptive Learning System. Behavioral Sciences, 12(7). https://doi.org/10.3390/bs12070244
Liu, J., Sun, D., Sun, J., Wang, J., & Yu, P. L. H. (2025). Designing a generative AI enabled learning environment for mathematics word problem solving in primary schools: Learning performance, attitudes and interaction. Computers and Education: Artificial Intelligence, 9. https://doi.org/10.1016/j.caeai.2025.100438
Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. B. F. (2016). Intelligence Unleashed An argument for AI in Education. Pearson.
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123
OECD. (2019). Education at a Glance 2019: OECD Indicators. OECD Publishing. https://doi.org/https://doi.org/10.1787/f8d7880d-en.
OECD. (2023). PISA 2022 Results: The State of Learning and Equity in Education (Vol. 1). OECD Publishing. https://doi.org/10.1787/53f23881-en
Oh, S. (2025). Integration of MATH41 and Generative AI in Pre-Service Mathematics Teacher Education: An Empirical Study on Lesson Design Competency. IEEE Access, 13, 128959–128973. https://doi.org/10.1109/ACCESS.2025.3586593
Page, M. J., McKenzie, J. E., & Bossuyt, P. M. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Partnership for 21st Century Skills. (2019). Framework for 21st Century Learning Definitions. Partnership for 21st Century Skills. Retrieved from http://static.battelleforkids.org/documents/p21/P21_Framework_DefinitionsBFK.pdf
Reimers, F., & Schleicher, A. (2020). A framework to guide an education response to the COVID-19 Pandemic of 2020. In Review of Educational Research (Number 3). OECD. https://doi.org/10.3102/00346543066003227
Sat, M. (2025). The impact of AI integration in project preparation in education course on pre-service teachers’ innovativeness, AI anxiety, attitudes, and acceptance. BMC Psychology, 13(1). https://doi.org/10.1186/s40359-025-03647-3
Toktarova, V. (2022). Model of Adaptive System for Mathematical Training of Students within eLearning Environment. International Journal of Emerging Technologies in Learning, 17(20), 99–117. https://doi.org/10.3991/ijet.v17i20.32923
Torres-Peña, R. C., Peña-González, D., Chacuto-López, E., Ariza, E. A., & Vergara, D. (2024). Updating Calculus Teaching with AI: A Classroom Experience. Education Sciences, 14(9). https://doi.org/10.3390/educsci14091019
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Wang, X., Huang, R. “Tammy,” Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., Ritzhaupt, A. D., & Martin, F. (2024). The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis. Journal of Educational Computing Research, 62(6), 1348–1382. https://doi.org/https://doi.org/10.1177/07356331241240459
Wu, H.-M., Yin, T., & Chan, Y.-J. (2025). Using a conversation-based agent system to foster math argumentation learning. Educational Technology Research and Development, 73(3), 1497–1518. https://doi.org/10.1007/s11423-025-10455-4
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? In International Journal of Educational Technology in Higher Education (Vol. 16, Number 1). Springer Netherlands. https://doi.org/10.1186/s41239-019-0171-0
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
Anand, U., Biswas, D., & Sardana, S. (2026). Adaptive Learning Systems for Education in Remote and Underserved Regions. In Adaptive Technologies for Sustainable Growth (pp. 504-507). CRC Press. https://doi.org/10.1201/9781003739937
Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130(2), 224–237. https://doi.org/10.1037/0096-3445.130.2.224
Bishop, M. J., Boling, E., Elen, J., & Svihla, V. (2020). Handbook of Research in Educational Communications and Technology: Learning Design: Fifth Edition. In Handbook of Research in Educational Communications and Technology: Learning Design: Fifth Edition. Springer International Publishing. https://doi.org/10.1007/978-3-030-36119-8
Boaler, J. (2016). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages and innovative teaching. Jossey-Bass.
Canonigo, A. M. (2024). Levering AI to enhance students’ conceptual understanding and confidence in mathematics. Journal of Computer Assisted Learning, 40(6), 3215–3229. https://doi.org/10.1111/jcal.13065
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Clark, R., Evans, P., & Moore, S. (2024). Implementing adaptive learning technologies: Practical strategies for mathematics education. Educational Technology Research and Development, 72(3), 456–478.
Egara, F. O., & Mosimege, M. (2024). Effect of flipped classroom learning approach on mathematics achievement and interest among secondary school students. Education and Information Technologies, 29(7), 8131–8150. https://doi.org/10.1007/s10639-023-12145-1
Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Homan, K., Marino, M., Vasquez, T., Taub, M., Hunt, J., & Tazi, Y. (2025). Artificial Intelligence Interventions in Mathematics Education: A Systematic Literature Review. Insights into Learning Disabilities, 22(1), 65–91. Retrieved from https://www.ldw-ild.org/images/Holman_et_alpdf.pdf
Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers and Education, 56(4), 1023–1031. https://doi.org/10.1016/j.compedu.2010.12.002
Kilpatrick, J., Swafford, J., & Findell, B. (2001). Adding it up: Helping children learn mathematics. National Academy Press.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. University of Durham.
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78. https://doi.org/10.3102/0034654315581420
Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers and Education, 191. https://doi.org/10.1016/j.compedu.2022.104646
Lim, L., Lim, S. H., & Lim, W. Y. R. (2022). A Rasch Analysis of Students’ Academic Motivation toward Mathematics in an Adaptive Learning System. Behavioral Sciences, 12(7). https://doi.org/10.3390/bs12070244
Liu, J., Sun, D., Sun, J., Wang, J., & Yu, P. L. H. (2025). Designing a generative AI enabled learning environment for mathematics word problem solving in primary schools: Learning performance, attitudes and interaction. Computers and Education: Artificial Intelligence, 9. https://doi.org/10.1016/j.caeai.2025.100438
Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. B. F. (2016). Intelligence Unleashed An argument for AI in Education. Pearson.
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123
OECD. (2019). Education at a Glance 2019: OECD Indicators. OECD Publishing. https://doi.org/https://doi.org/10.1787/f8d7880d-en.
OECD. (2023). PISA 2022 Results: The State of Learning and Equity in Education (Vol. 1). OECD Publishing. https://doi.org/10.1787/53f23881-en
Oh, S. (2025). Integration of MATH41 and Generative AI in Pre-Service Mathematics Teacher Education: An Empirical Study on Lesson Design Competency. IEEE Access, 13, 128959–128973. https://doi.org/10.1109/ACCESS.2025.3586593
Page, M. J., McKenzie, J. E., & Bossuyt, P. M. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Partnership for 21st Century Skills. (2019). Framework for 21st Century Learning Definitions. Partnership for 21st Century Skills. Retrieved from http://static.battelleforkids.org/documents/p21/P21_Framework_DefinitionsBFK.pdf
Reimers, F., & Schleicher, A. (2020). A framework to guide an education response to the COVID-19 Pandemic of 2020. In Review of Educational Research (Number 3). OECD. https://doi.org/10.3102/00346543066003227
Sat, M. (2025). The impact of AI integration in project preparation in education course on pre-service teachers’ innovativeness, AI anxiety, attitudes, and acceptance. BMC Psychology, 13(1). https://doi.org/10.1186/s40359-025-03647-3
Toktarova, V. (2022). Model of Adaptive System for Mathematical Training of Students within eLearning Environment. International Journal of Emerging Technologies in Learning, 17(20), 99–117. https://doi.org/10.3991/ijet.v17i20.32923
Torres-Peña, R. C., Peña-González, D., Chacuto-López, E., Ariza, E. A., & Vergara, D. (2024). Updating Calculus Teaching with AI: A Classroom Experience. Education Sciences, 14(9). https://doi.org/10.3390/educsci14091019
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Wang, X., Huang, R. “Tammy,” Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., Ritzhaupt, A. D., & Martin, F. (2024). The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis. Journal of Educational Computing Research, 62(6), 1348–1382. https://doi.org/https://doi.org/10.1177/07356331241240459
Wu, H.-M., Yin, T., & Chan, Y.-J. (2025). Using a conversation-based agent system to foster math argumentation learning. Educational Technology Research and Development, 73(3), 1497–1518. https://doi.org/10.1007/s11423-025-10455-4
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? In International Journal of Educational Technology in Higher Education (Vol. 16, Number 1). Springer Netherlands. https://doi.org/10.1186/s41239-019-0171-0
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629