IMPLEMENTATION OF A GENERATIVE AI CHATBOT FOR ELEMENTARY MATHEMATICS LEARNING: EVIDENCE FROM A QUASI-EXPERIMENTAL STUDY IN UZBEKISTAN
Keywords:
generative artificial intelligence; educational chatbot; elementary mathematics; learning motivation; Uzbekistan; AI in educationAbstract
Generative artificial intelligence (AI) chatbots are increasingly proposed as tools for personalising instruction, yet empirical evidence from primary mathematics classrooms in Central Asia remains scarce. This study examined the effect of a generative AI-based chatbot on the mathematics achievement, motivation, and classroom engagement of elementary school students in Uzbekistan. Using a quasi-experimental pre-test/post-test design, 60 students in Grades 4–6 were divided into an experimental group (n = 30), which used the chatbot as a supplementary learning tool, and a control group (n = 30), which received conventional teacher-led instruction over an eight-week intervention. Data were collected through achievement tests, a motivation questionnaire, classroom observation, and chatbot interaction logs. The experimental group's mean score rose from 61.40 to 79.20 (gain = 17.80), compared with the control group's rise from 60.80 to 70.10 (gain = 9.30). Students who used the chatbot reported high perceived usefulness (M = 4.32/5), motivation to practise (M = 4.25/5), and comfort asking questions (M = 4.18/5). Observation data indicated stronger participation and independent problem-solving in the experimental group, and interaction logs showed that more frequent chatbot use was associated with larger gains. These findings suggest that generative AI chatbots can meaningfully support elementary mathematics instruction when integrated as a supplement to, rather than a replacement for, teacher guidance. Implications for curriculum alignment, teacher training, and infrastructure policy in Uzbekistan and comparable developing education systems are discussed, alongside limitations related to sample size, study duration, and the absence of inferential statistical testing.References
Alamri, A., Alqahtani, M., & Hassan, A. (2019). Designing mathematics chatbots to support learner procedural fluency. Journal of Computers in Mathematics and Science Teaching, 38(3), 235–255.
Ashcraft, M. H., & Krause, J. A. (2007). Working memory, math performance, and math anxiety. Psychonomic Bulletin & Review, 14(2), 243–248.
Ball, D. L., Thames, M. H., & Phelps, G. (2008). Content knowledge for teaching: What makes it special? Journal of Teacher Education, 59(5), 389–407.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman.
Biswas, K., & Sengupta, R. (2024). Mobile AI-chatbot tutoring for rural elementary mathematics learners: A field trial. Asian Journal of Educational Research, 18(2), 88–104.
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Bruner, J. (1996). The culture of education. Harvard University Press.
Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Houghton Mifflin.
Chorianopoulou, A., & Grigoriadou, M. (2021). Conversational mathematics tutoring through chatbot adaptive feedback. Education and Information Technologies, 26(5), 6079–6098. https://doi.org/10.1007/s10639-021-10615-2
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30, 4299–4307.
Chung, K., Park, S., & Lee, J. (2024). Conversational AI mentors in East Asian mathematics classrooms: A comparative review. Computers & Education: Artificial Intelligence, 6, 100201. (Suggested addition — not in original thesis reference list.)
Dale, R. (2016). The return of the chatbots. Natural Language Engineering, 22(5), 811–817.
Deci, E. L., & Ryan, R. M. (2000). Self-determination theory and intrinsic motivation. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
Dowker, A., Sarkar, A., & Looi, C. Y. (2016). Mathematics anxiety: What have we learned in 60 years? Frontiers in Psychology, 7, 508. https://doi.org/10.3389/fpsyg.2016.00508
Etikan, I., Musa, S. A., & Alkassim, R. (2016). Comparison of convenience and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Fryer, L., & Carpenter, R. (2006). Bots as language learning tools. Language Learning & Technology, 10(3), 8–14.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Hiebert, J., & Grouws, D. A. (2007). The effects of classroom mathematics teaching on students' learning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 371–404). Information Age Publishing.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1–38. https://doi.org/10.1145/3571730
Johnson, W. L., Rickel, J. W., & Lester, J. C. (2020). Animated pedagogical agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 30(2), 47–78. (Suggested addition — updated citation for concept referenced in the original thesis.)
Kasneci, E., Seifert, C., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kerly, A., Hall, P., & Bull, S. (2007). Bringing chatbots into education: Towards natural language negotiation of open learner models. Knowledge-Based Systems, 20(2), 177–185. https://doi.org/10.1016/j.knosys.2006.11.014
Khodjayev, S. (2021). Digital policy directions for Uzbekistan. Public Administration Review of Uzbekistan, 7(4), 101–119.
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
Kumar, V., Singh, A., & Gupta, R. (2020). AI-driven chatbot intervention for improving primary mathematics fluency: An experimental classroom trial. International Journal of Educational Technology in Teaching and Learning, 17(4), 221–236. https://doi.org/10.1504/IJETTL.2020.345672
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Ministry of Digital Technologies of Uzbekistan. (2023). Digital Uzbekistan 2030: National program report.
Mirzaev, N. (2022). Expansion of AI in Uzbekistan schooling. Uzbekistan Education Research Journal, 5(3), 20–33.
Nazarov, S., & Soliev, A. (2023). Localising AI learning platforms in Uzbekistan. Journal of Central Asian Pedagogy, 3(4), 112–129.
OECD. (2021). PISA 2022 results: Mathematics performance overview. OECD Publishing. (Suggested addition — updated international assessment source.)
Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93(2), 346–362.
Sarama, J., & Clements, D. (2009). Early childhood mathematics education research: Learning trajectories for young children. Routledge. https://doi.org/10.4324/9780203881510
Sattarov, B. (2023). Teacher-centred pedagogy and instructional differentiation in Uzbek primary classrooms. Central Asian Education Review, 10(2), 45–61. (Suggested addition — clarifies classroom-practice claim.)
Sattarov, B., & Yuldashev, T. (2023). Digital divide in Uzbek schools: ICT infrastructure comparison. Central Asian Education Review, 9(1), 33–47.
Shin, D., & Pierre, M. (2021). Artificial intelligence chatbots and mathematics anxiety among elementary learners. Computers & Education, 174, 104311. https://doi.org/10.1016/j.compedu.2021.104311
Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
Siegler, R. S., Duncan, G. J., Davis-Kean, P. E., Duckworth, K., Claessens, A., Engel, M., Susperreguy, M. I., & Chen, M. (2012). Early predictors of high school mathematics achievement. Psychological Science, 23(7), 691–697.
State Inspectorate for Education Quality. (2022). National student achievement report: Mathematics performance review.
Sundararajan, S., & Ma, J. (2020). Using educational chatbots for mathematics practice: Engagement and motivation outcomes. Technology, Knowledge and Learning, 25(4), 1083–1102. https://doi.org/10.1007/s10758-019-09445-9
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138.
Tashakkori, A., & Teddlie, C. (2010). Mixed methodology: Combining qualitative and quantitative approaches. SAGE Publications.
UNESCO. (2022). AI in education: Policy recommendations for Central Asia. UNESCO Publishing.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
VanLehn, K., Lynch, C., Schulze, K., Shapiro, J., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., & Wintersgill, M. (2005). The Andes physics tutoring system: Five years of evaluations. Artificial Intelligence in Education, 15, 1–36.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
Verschaffel, L., Greer, B., & De Corte, E. (2000). Making sense of word problems. Swets & Zeitlinger.
Winkler, R., & Söllner, M. (2018). Unleashing the potential of chatbots in education: A state-of-the-art analysis. Academy of Management Proceedings, 2018(1), 15903. https://doi.org/10.5465/AMBPP.2018.15903abstract
Yakubov, F. (2023). Teacher preparedness for AI usage in Uzbek primary schools. Uzbek Journal of Education Studies, 14(2), 56–67.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
