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Social Sciences and Humanities Issue (Vietnamese)
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28/04/2026
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Nguyen, H. H., Tran, T. N. T., Nguyen, N. D., & Le, V. V. T. (2026). Applying ChatGPT in teaching mathematical modeling
of conditional probability to enhance students’ learning motivation. Dong Thap University Journal of Science, 15(6), 53-67. https://doi.org/10.52714/dthu.sch.2755.1822
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Applying ChatGPT in teaching mathematical modeling
of conditional probability to enhance students’ learning motivation
Main Article Content
Abstract
In the context of current educational innovation, the integration of artificial intelligence into the teaching and learning process is opening up new approaches to developing c competencies and enhancing students' interest. Conditional probability is considered a difficult topic, often causing obstacles for students and leading to a decrease in learning motivation. This study aims to exploit the ability to apply ChatGPT in creating questions and supporting mathematical modeling for conditional probability content, aiming to improve students' learning motivation. Based on the theoretical research method, the article proposes and illustrates the teaching and learning process using ChatGPT to design questions and support modeling in conditional probability content. The results obtained clearly analyzed each step of the process affecting students' learning motivation through increasing their initiative, self-study ability and interest when receiving instant feedback from ChatGPT in learning mathematics.
Keywords
ChatGPT, conditional probability, mathematical modeling, learning motivation, upper secondary education.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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Anggara, B., Priatna, N., & Juandi, D. (2018). Learning difficulties of senior high school students based on probability understanding levels. Journal of Physics: Conference Series, 1013, 012116. https://doi.org/10.1088/1742-6596/1013/1/012116
Annamalai, N., Bervell, B., Mireku, D. O., & Andoh, R. P. (2025). Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory. Computers and Education: Artificial Intelligence, 8, 100346. https://doi.org/10.1016/j.caeai.2024.100346
Batanero, C., & Serrano, L. (1999). The meaning of randomness for secondary school students. Journal for Research in Mathematics Education, 30(5). https://doi.org/10.2307/749774
Baumeister, R., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), pp.497-529. https://doi.org/10.1037/0033-2909.117.3.497
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616-630. https://doi.org/10.1007/s11528-022-00715-y
Chan, C. K., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00284-4
Deci, E. L. (1975). Intrinsic motivation. New York: Plenum.
Eccles, J. S. (2006). A motivational perspective on school achievement. In R. J. Sternberg & R. F. Subotnik (Eds.), Optimizing student success in schools with the other three Rs: Reasoning, Resilience, and Responsibility (pp. 199 - 224). Greenwich, Connecticut: Information Age Publishing.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Eccles, J. S., & Wigfield, A. (2020). From Expectancy-Value Theory to Situated Expectancy Value Theory: A Developmental, Social Cognitive, and Sociocultural Perspective on Motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Eccles, J. S., & Wigfield, A. (2024). The Development, Testing, and Refinement of Eccles, Wigfield, and Colleagues’ Situated Expectancy-Value Model of Achievement Performance and Choice. Educational Psychology Review, 36(51), 36-51. https://doi.org/10.1007/s10648-024-09888-9
Harter, S. (1978). Effectance motivation reconsidered: Toward a developmental model. Human Development, 21(1), pp.34-64. https://doi.org/10.1159/000271574
Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher education students’ task motivation in the generative artificial intelligence context: The case of ChatGPT. Information, 15(1), 33. https://doi.org/10.3390/info15010033
Huerta, M. P. (2009). On conditional probability problem solving research – Structures and contexts. International Electronic Journal of Mathematics Education, 4(3), 163-194. https://doi.org/10.29333/iejme/235
Jackson, R. (2022). AI in Education: Revolutionizing Learning and Teaching. Journal of Arts, Society, and Education Studies, 4(4), 116. https://doi.org/10.25215/9358094575.02
Kurtic, V. (2024). Enhancing students' confidence and understanding in probability through ChatGPT: an analysis of ai's impact on learning experiences. saZnanje Journal, 4, 942-957.
Lê, T. H. C. (2014). Mô hình hóa trong dạy học khái niệm đạo hàm. Tạp chí Khoa học - Trường Đại học Sư phạm TP Hồ Chí Minh, 65, 5-18. https://vjol.info.vn/index.php/sphcm/article/view/18453
Lê, V. T. (2005). Phương pháp dạy học môn Toán ở trường phổ thông. NXB Đại học Quốc gia TP Hồ Chí Minh.
León, J., Núnez., J. L., & Liew, J. (2015). Self-determination and STEM Education: Effects of Autonomy, Motivation, and Self-regulated Learning on High School Math Achievement. Learning and Individual Differences, 43, 156-163. https://doi.org/10.1016/j.lindif.2015.08.017
Li, J., & Xue, E. (2023). Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors. Behavioral Sciences, 13(1), 59. https://doi.org/10.3390/bs13010059
Memnun., D. S., Ozbilen, O., & Dinc, E. (2019). A Qualitative Research on the Difficulties and Failures about Probability Concepts of High School Students. Journal of Educational, 5(1). https://doi.org/10.5296/jei.v5i1.14146
Mohamed, A., Shaaban, T., Bakry, S., Gámez, F. D. G. G., Strzelecki, A. (2024). Empowering the Faculty of Education Students: Applying AI’s Potential for Motivating and Enhancing Learning. Innovative Higher Education, 50, 587-609. https://doi.org/10.1007/s10755-024-09747-z
Nguyễn, A. Q. (2022). Dạy học Xác suất có điều kiện ở lớp 12 theo Chương trình Giáo dục phổ thông môn Toán 2018. Tạp chí khoa học giáo dục Việt Nam. https://doi.org/10.15625/2615-8957/12211107
Nguyễn, D. N. (2015). Quy trình mô hình hóa trong dạy học Toán trường phổ thông. Tạp chí Khoa học ĐHQGHN: Nghiên cứu Giáo dục, 31(3), 1-10.
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(4). https://doi.org/10.1186/s41239-022-00372-4
Pavlova, N. H. (2024). Flipped dialogic learning method with ChatGPT: A case study. International Electronic Journal of Mathematics Education, 19(1), em0764. https://doi.org/10.29333/iejme/14025
Pepin, B., Buchholtz, N., & Salinas-Hernández, U. (2025). A scoping survey of ChatGPT in mathematics education. Digital Experiences in Mathematics Education, 11(1), 9-41. https://doi.org/10.1007/s40751-025-00172-1
Peters, J. (2025, July 29). ChatGPT’s new AI study mode won’t just give you the answer. The Verge. Truy cập từ https://www.theverge.com/news/715493/openai-chatgpt-ai-study-mode-answer?utm_source=chatgpt.com
Phạm, T. M. H., & Trần, T. N. G. (2020). Vận dụng phương pháp mô hình hóa trong giảng dạy học phần đại số sơ cấp ngành sư phạm toán. Tạp chí Khoa học Đại học Đồng Tháp, 10(1), 26-32. https://doi.org/10.52714/dthu.10.1.2021.841
Prodromou, T. (2016). Secondary school students’ reasoning about conditional probability, samples, and sampling procedures. STATISTICS EDUCATION RESEARCH JOURNAL, 15(2), 106-125. https://doi.org/10.52041/serj.v15i2.243
Ryan, R. M., & Deci, E. L. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. American Psychologist, 55(1), pp.68-78. https://doi.org/10.1037/0003-066X.55.1.68
Schunk, D. H. (2000). Coming to Terms with Motivation Constructs. Contemporary Educational Psychology, 25(1), 116-119. http://dx.doi.org/10.1006/ceps.1999.1018
Spratt, M., Humphreys, G., & Chan, V. (2002). Autonomy and motivation: which comes first?. Language teaching Research, 6(3), pp.245-266. https://doi.org/10.1191/1362168802lr106oa
Thipyarat, S. (2025). Exploring the roles of ChatGPT in probability education. Journal of Innovative Learning, 1(2), 1-11. https://il.mahidol.ac.th/jil_systems/index.php/01/article/view/37
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Wang, K., Cui, W., & Yuan, X. (2025). Artificial intelligence in higher education: The impact of need satisfaction on artificial intelligence literacy mediated by self-regulated learning strategies. Behavioral Sciences, 15(2), 165. https://doi.org/10.3390/bs15020165
White, R. W. (1963). Ego and reality in psychoanalytic theory. International Universities Press. 3(3, Whole No. 11), pp.1–210.
Williams, A. (2024). Delivering effective student feedback in higher education: An evaluation of the challenges and best practice. International Journal of Research in Education and Science, 10(2), 473-501. https://doi.org/10.46328/ijres.3404
Xia, Q., Chiu, T. K., Chai, C. S., & Xie, K. (2023). The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot. British Journal of Educational Technology, 54(4), 967-986. https://doi.org/10.1111/bjet.13305
Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871-1885. https://doi.org/10.1111/jcal.12988
Zhao, S., Shen, Y., & Qi, Z. (2023). Academic Journal of Mathematical Sciences, 4(5). https://doi.org/10.25236/ajms.2023.040506
Almarashdi, H. S., Jarrah, A. M., Abu Khurma, O., & Gningue, S. M. (2024). Unveiling the potential: A systematic review of ChatGPT in transforming mathematics teaching and learning. Eurasia Journal of Mathematics, Science and Technology Education, 20(12). https://doi.org/10.29333/ejmste/15739
Ancker, J. S. (2006). The Language of Conditional Probability. Journal of Statistics Education, 14(2). https://doi.org/10.1080/10691898.2006.11910584
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.
Anggara, Y., Purwanto, P., & Budiyono, B. (2020). Learning difficulties of senior high school students based on probability understanding levels. 4th International Seminar of Mathematics, Science and Computer Science Education. Journal of Physics: Conference Series, 1013. https://doi.org/10.1088/1742-6596/1013/1/012116
Anggara, B., Priatna, N., & Juandi, D. (2018). Learning difficulties of senior high school students based on probability understanding levels. Journal of Physics: Conference Series, 1013, 012116. https://doi.org/10.1088/1742-6596/1013/1/012116
Annamalai, N., Bervell, B., Mireku, D. O., & Andoh, R. P. (2025). Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory. Computers and Education: Artificial Intelligence, 8, 100346. https://doi.org/10.1016/j.caeai.2024.100346
Batanero, C., & Serrano, L. (1999). The meaning of randomness for secondary school students. Journal for Research in Mathematics Education, 30(5). https://doi.org/10.2307/749774
Baumeister, R., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), pp.497-529. https://doi.org/10.1037/0033-2909.117.3.497
Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616-630. https://doi.org/10.1007/s11528-022-00715-y
Chan, C. K., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00284-4
Deci, E. L. (1975). Intrinsic motivation. New York: Plenum.
Eccles, J. S. (2006). A motivational perspective on school achievement. In R. J. Sternberg & R. F. Subotnik (Eds.), Optimizing student success in schools with the other three Rs: Reasoning, Resilience, and Responsibility (pp. 199 - 224). Greenwich, Connecticut: Information Age Publishing.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
Eccles, J. S., & Wigfield, A. (2020). From Expectancy-Value Theory to Situated Expectancy Value Theory: A Developmental, Social Cognitive, and Sociocultural Perspective on Motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Eccles, J. S., & Wigfield, A. (2024). The Development, Testing, and Refinement of Eccles, Wigfield, and Colleagues’ Situated Expectancy-Value Model of Achievement Performance and Choice. Educational Psychology Review, 36(51), 36-51. https://doi.org/10.1007/s10648-024-09888-9
Harter, S. (1978). Effectance motivation reconsidered: Toward a developmental model. Human Development, 21(1), pp.34-64. https://doi.org/10.1159/000271574
Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher education students’ task motivation in the generative artificial intelligence context: The case of ChatGPT. Information, 15(1), 33. https://doi.org/10.3390/info15010033
Huerta, M. P. (2009). On conditional probability problem solving research – Structures and contexts. International Electronic Journal of Mathematics Education, 4(3), 163-194. https://doi.org/10.29333/iejme/235
Jackson, R. (2022). AI in Education: Revolutionizing Learning and Teaching. Journal of Arts, Society, and Education Studies, 4(4), 116. https://doi.org/10.25215/9358094575.02
Kurtic, V. (2024). Enhancing students' confidence and understanding in probability through ChatGPT: an analysis of ai's impact on learning experiences. saZnanje Journal, 4, 942-957.
Lê, T. H. C. (2014). Mô hình hóa trong dạy học khái niệm đạo hàm. Tạp chí Khoa học - Trường Đại học Sư phạm TP Hồ Chí Minh, 65, 5-18. https://vjol.info.vn/index.php/sphcm/article/view/18453
Lê, V. T. (2005). Phương pháp dạy học môn Toán ở trường phổ thông. NXB Đại học Quốc gia TP Hồ Chí Minh.
León, J., Núnez., J. L., & Liew, J. (2015). Self-determination and STEM Education: Effects of Autonomy, Motivation, and Self-regulated Learning on High School Math Achievement. Learning and Individual Differences, 43, 156-163. https://doi.org/10.1016/j.lindif.2015.08.017
Li, J., & Xue, E. (2023). Dynamic interaction between student learning behaviour and learning environment: Meta-analysis of student engagement and its influencing factors. Behavioral Sciences, 13(1), 59. https://doi.org/10.3390/bs13010059
Memnun., D. S., Ozbilen, O., & Dinc, E. (2019). A Qualitative Research on the Difficulties and Failures about Probability Concepts of High School Students. Journal of Educational, 5(1). https://doi.org/10.5296/jei.v5i1.14146
Mohamed, A., Shaaban, T., Bakry, S., Gámez, F. D. G. G., Strzelecki, A. (2024). Empowering the Faculty of Education Students: Applying AI’s Potential for Motivating and Enhancing Learning. Innovative Higher Education, 50, 587-609. https://doi.org/10.1007/s10755-024-09747-z
Nguyễn, A. Q. (2022). Dạy học Xác suất có điều kiện ở lớp 12 theo Chương trình Giáo dục phổ thông môn Toán 2018. Tạp chí khoa học giáo dục Việt Nam. https://doi.org/10.15625/2615-8957/12211107
Nguyễn, D. N. (2015). Quy trình mô hình hóa trong dạy học Toán trường phổ thông. Tạp chí Khoa học ĐHQGHN: Nghiên cứu Giáo dục, 31(3), 1-10.
Ouyang, F., Wu, M., Zheng, L., Zhang, L., & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. International Journal of Educational Technology in Higher Education, 20(4). https://doi.org/10.1186/s41239-022-00372-4
Pavlova, N. H. (2024). Flipped dialogic learning method with ChatGPT: A case study. International Electronic Journal of Mathematics Education, 19(1), em0764. https://doi.org/10.29333/iejme/14025
Pepin, B., Buchholtz, N., & Salinas-Hernández, U. (2025). A scoping survey of ChatGPT in mathematics education. Digital Experiences in Mathematics Education, 11(1), 9-41. https://doi.org/10.1007/s40751-025-00172-1
Peters, J. (2025, July 29). ChatGPT’s new AI study mode won’t just give you the answer. The Verge. Truy cập từ https://www.theverge.com/news/715493/openai-chatgpt-ai-study-mode-answer?utm_source=chatgpt.com
Phạm, T. M. H., & Trần, T. N. G. (2020). Vận dụng phương pháp mô hình hóa trong giảng dạy học phần đại số sơ cấp ngành sư phạm toán. Tạp chí Khoa học Đại học Đồng Tháp, 10(1), 26-32. https://doi.org/10.52714/dthu.10.1.2021.841
Prodromou, T. (2016). Secondary school students’ reasoning about conditional probability, samples, and sampling procedures. STATISTICS EDUCATION RESEARCH JOURNAL, 15(2), 106-125. https://doi.org/10.52041/serj.v15i2.243
Ryan, R. M., & Deci, E. L. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. American Psychologist, 55(1), pp.68-78. https://doi.org/10.1037/0003-066X.55.1.68
Schunk, D. H. (2000). Coming to Terms with Motivation Constructs. Contemporary Educational Psychology, 25(1), 116-119. http://dx.doi.org/10.1006/ceps.1999.1018
Spratt, M., Humphreys, G., & Chan, V. (2002). Autonomy and motivation: which comes first?. Language teaching Research, 6(3), pp.245-266. https://doi.org/10.1191/1362168802lr106oa
Thipyarat, S. (2025). Exploring the roles of ChatGPT in probability education. Journal of Innovative Learning, 1(2), 1-11. https://il.mahidol.ac.th/jil_systems/index.php/01/article/view/37
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Wang, K., Cui, W., & Yuan, X. (2025). Artificial intelligence in higher education: The impact of need satisfaction on artificial intelligence literacy mediated by self-regulated learning strategies. Behavioral Sciences, 15(2), 165. https://doi.org/10.3390/bs15020165
White, R. W. (1963). Ego and reality in psychoanalytic theory. International Universities Press. 3(3, Whole No. 11), pp.1–210.
Williams, A. (2024). Delivering effective student feedback in higher education: An evaluation of the challenges and best practice. International Journal of Research in Education and Science, 10(2), 473-501. https://doi.org/10.46328/ijres.3404
Xia, Q., Chiu, T. K., Chai, C. S., & Xie, K. (2023). The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot. British Journal of Educational Technology, 54(4), 967-986. https://doi.org/10.1111/bjet.13305
Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871-1885. https://doi.org/10.1111/jcal.12988
Zhao, S., Shen, Y., & Qi, Z. (2023). Academic Journal of Mathematical Sciences, 4(5). https://doi.org/10.25236/ajms.2023.040506
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