Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377–386. https://doi.org/10.1162/qss_a_00019
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100002. https://doi.org/10.1016/j.caeai.2020.100002
Christopoulos, A., & Sprangers, P. (2021). Integration of educational technology during the Covid-19 pandemic: An analysis of teacher and student receptions. Cogent Education, 8(1), Article 1964690. https://doi.org/10.1080/2331186X.2021.1964690
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Egara, F. O., & Mosimege, M. (2024). Exploring the integration of artificial intelligence-based ChatGPT into mathematics instruction: Perceptions, challenges, and implications for educators. Education Sciences, 14(7), Article 742. https://doi.org/10.3390/educsci14070742
Getenet, S. (2024). Pre-service teachers and ChatGPT in multistrategy problem-solving: Implications for mathematics teaching in primary schools. International Electronic Journal of Mathematics Education, 19(1), Article em0766. https://doi.org/10.29333/iejme/14141
Huang, X., Craig, S. D., Xie, J., Graesser, A., & Hu, X. (2016). Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program. Learning and Individual Differences, 47, 258–265. https://doi.org/10.1016/j.lindif.2016.01.012
Huang, X., & Qiao, C. (2024). Enhancing computational thinking skills through artificial intelligence education at a STEAM high school. Science & Education, 33(2), 383–403. https://doi.org/10.1007/s11191-022-00392-6
Hwang, G.-J., & Tu, Y.-F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), Article 584. https://doi.org/10.3390/math9060584
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100001. https://doi.org/10.1016/j.caeai.2020.100001
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
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, Article 100438. https://doi.org/10.1016/j.caeai.2025.100438
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
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2014). Effectiveness of Cognitive Tutor Algebra I at scale. Educational Evaluation and Policy Analysis, 36(2), 127–144. https://doi.org/10.3102/0162373713507480
Rutatola, E. P., Stroeken, K., & Belpaeme, T. (2025). “Habari, colleague!”: A qualitative exploration of the perceptions of primary school mathematics teachers in Tanzania regarding the use of social robots. Applied Sciences, 15(15), Article 8483. https://doi.org/10.3390/app15158483
Venugopalan, D., Yan, Z., Borchers, C., Lin, J., & Aleven, V. (2025). Combining large language models with tutoring system intelligence: A case study in caregiver homework support. In Proceedings of the 15th International Conference on Learning Analytics and Knowledge (pp. 373–383). Association for Computing Machinery. https://doi.org/10.1145/3706468.3706516
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments, 31(2), 793–803. https://doi.org/10.1080/10494820.2020.1808794
Xing, W., Song, Y., Li, C., Liu, Z., Zhu, W., & Oh, H. (2025). Development of a generative AI-powered teachable agent for middle school mathematics learning: A design-based research study. British Journal of Educational Technology, 56(5), 2043–2077. https://doi.org/10.1111/bjet.13586
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? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, Article 8812542. https://doi.org/10.1155/2021/8812542
Zhang, F., Li, C., Henkel, O., Xing, W., Baral, S., Heffernan, N., & Li, H. (2025). Math-LLMs: AI cyberinfrastructure with pre-trained transformers for math education. International Journal of Artificial Intelligence in Education, 35(2), 509–532. https://doi.org/10.1007/s40593-024-00416-y
Zheng, G., Fancsali, S. E., Ritter, S., & Berman, S. R. (2019). Using instruction-embedded formative assessment to predict state summative test scores and achievement levels in mathematics. Journal of Learning Analytics, 6(2), 153–174. https://doi.org/10.18608/jla.2019.62.11
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629