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Exploring the Impact of ChatGPT on Mathematics Performance: The Influential Role of Student Interest
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This investigation examines the influence of ChatGPT on mathematics achievement, with a specific focus on the moderating role of students' interest in mathematics.A sample of 250 students, encompassing undergraduates pursuing a Bachelor of Science and postgraduates engaged in Masters of Philosophy and Doctor of Philosophy programs in Mathematics Education at Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development (AAMUSTED), Kumasi-Ghana, was selected through random sampling.Employing a quantitative methodology, data were collected via structured questionnaires and analyzed using Amos software, version 23, to test the hypothesized relationships.The findings revealed that student interest in mathematics significantly and positively correlates with the use of ChatGPT, as evidenced by a p-value of less than 1%.Conversely, ChatGPT's direct influence on mathematics achievement was found to be negative, though not statistically significant, with a p-value of less than 1%.Furthermore, a direct, positive, and statistically significant relationship between students' interest in mathematics and their achievement in the subject was observed, with a p-value of less than 1%.Notably, the study identified a statistically significant positive moderation effect of students' interest on the association between ChatGPT usage and mathematics achievement, underlined by a p-value of less than 1%.The findings advocate for a cautious integration of ChatGPT in mathematics education, emphasizing that reliance on artificial intelligence should complement, not replace, traditional learning modalities.Additionally, it is suggested that future research might benefit from employing surveys or self-evaluation tools beyond questionnaires to gather data.This study contributes to the existing body of knowledge by highlighting the nuanced role of student interest in leveraging technology-enhanced learning tools for academic success in mathematics.
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“However, more complex tasks involving deeper understanding and application might require more active engagement and critical thinking, where reliance on AI alone may not yield significant improvements in knowledge transfer (Asare et al., 2023; Soderstrom and Bjork, 2015; Urban et al., 2024).”
From Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (2025) · cites this paper as Asare et al., 2023
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“Combined with some previous studies which found that ChatGPT cannot significantly improve performance (e.g., Asare et al. (2023)), we posit that providing clear and well-structured rubric or criteria is critical for the effective impact of ChatGPT on learning performance.”
From Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (2025) · cites this paper as Asare et al. (2023)
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“Combined with some previous studies which found that ChatGPT cannot significantly improve performance (e.g., Asare et al. (2023)), we posit that providing clear and well-structured rubric or criteria is critical for the effective impact of ChatGPT on learning performance.”
From Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance (2025) · cites this paper as Asare et al. (2023)
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Research works printed but not linked: 18
- Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., & Mohammed, A. A. (2023). Impact of ChatGPT on learning motivation: Teachers and students' voices. J. English Stud. Arabia Felix, 2(1), 41-49. https://doi.org/10.56540/jesaf.v2i1. · DOI
- Amoako, T., Sheng, Z. H., Dogbe, C. S. K., & Pomegbe, W. W. K. (2022). Assessing the moderation role of ICT in the relationship between supply chain integration and SME performance. J. Ind. Integr. Manage., 7(2), 203-233. https://doi.org/10.1142/S2424862221500160. · DOI
- Arhin, D. & Yanney, E. G. (2020). Relationship between students' interest and academic performance in mathematics: A study of Agogo State College. Global Sci. J., 8(6), 389-396.
- Arthur, Y. D., Dogbe, C. S. K., & Asiedu-Addo, S. K. (2022). Enhancing performance in mathematics through motivation, peer assisted learning, and teaching quality: The mediating role of student interest. Eurasia J. Math. Sci. Technol. Educ., 18(2), em2072. https://doi.org/10.29333/EJMSTE/11509. · DOI
- Dao, X. Q. & Le, N. B. (2023). Investigating the effectiveness of ChatGPT in Mathematical reasoning and problem solving: Evidence from the Vietnamese national high school graduation examination. ArXiv, 2306.06331. https://doi.org/10.48550/arXiv.2306.06331. · DOI · Paper page
- Dogbe, C. S. K., Bamfo, B. A., & Opoku, E. (2019). Consumer ethnocentrism: Empirical evidence from Ghana. Int. J. Res. Mark. Manag. Sales, 1(1), 23-29. https://doi.org/10.33545/26633329.2019.v1.i2a. · DOI
- Gómez-Garcí a, M., Hossein-Mohand, H., Trujillo-Torres, J. M., Hossein-Mohand, H., & Aznar-Dí az, I. (2020). Technological factors that influence the mathematics performance of secondary school students. Math., 8(11), 1935. https://doi.org/10.3390/math8111935. · DOI
- Kalpana, M. S. & Malathi, M. V. (2019). A study on interest in mathematics interest and its relation to academic achievement in mathematics among higher secondary students. Int. J. Trend Sci. Res. Dev., 3(4), 1246-1254. https://doi.org/10.31142/ijtsrd25113. · DOI
- Nguyen, P., Nguyen, P., Bruneau, P., Cao, L., Wang, J., & Truong, H. (2023). Evaluation of mathematics performance of google bard on the mathematics test of the Vietnamese national high school graduation examination. Authorea Prepr. https://doi.org/10.36227/techrxiv.23691876.v1. · DOI
- Pitsia, V., Biggart, A., & Karakolidis, A. (2017). The role of students' self-beliefs, motivation and attitudes in predicting mathematics achievement: A multilevel analysis of the programme for international student assessment data. Learn. Individual Differences, 55, 163-173. https://doi.org/10.1016/j.lindif.2017.03.014. · DOI
- Shrestha, N. (2021). Factor analysis as a tool for survey analysis. Am. J. Appl. Math. Stat., 9(1), 4-11. https://doi.org/10.12691/ajams-9-1-2. · DOI
- Silinskas, G. & Kikas, E. (2019). Parental involvement in math homework: Links to children's performance and motivation. Scandinavian J. Educational Res., 63(1), 17-37. https://doi.org/10.1080/00313831.2017.1324901. · DOI
- Union J. Ilmiah Pendidik. Mat., 11(2), 305-316. https://doi.org/10.30738/union.v11i2.14898. · DOI
- Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia J. Math. Sci. Technol. Educ., 19(7), em2286. https://doi.org/10.29333/ejmste/13272. · DOI
- Wong, S. L. & Wong, S. L. (2019). Relationship between interest and mathematics performance in a technology- enhanced learning context in Malaysia. Res. Pract. Technol. Enhanced Learn., 14(1), 1-13. https://doi.org/10.1186/s41039-019-0114-3. · DOI
- Woodhouse, D. & Charlesworth, A. (2023). Can ChatGPT predict future interest rate decisions? SSRN Electron. J. http://doi.org/10.2139/ssrn.4572831. · DOI
- Yamane, I. & Sato, K. (1967). Effect of temperature on the decomposition of organic substances in flooded soil. Soil Sci. Plant Nutr., 13(4), 94-100. https://doi.org/10.1080/00380768.1967.10431981. · DOI
- Zafrullah, Z., Hakim, M. L., & Angga, M. (2023). ChatGPT open AI: Analysis of mathematics education students learning interest. J. Technol. Global, 1(1), 1-10. https://penaeducentre.com/index.php/JTeG.
Cited by
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13544 · Cited by 805
Exploring ChatGPT ‐Facilitated Scaffolding in Undergraduates' Mathematical Problem Solving
Zhou, R., He, X., Fan, Q., Li, Y., Li, Y., Xiao, X., & Fang, J. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70077 · Cited by 15
Metadata
- Authors
- Asare, B., Arthur, Y. D., & Boateng, F. O.
- Affiliations
- University of Skills Training and Entrepreneurial Development (GH)
- Corresponding author
- Asare, Bright
- Venue
- Education Science and Management
- Publisher
- Acadlore Publishing Services Limited
- Published
- 2023-12-31
- Volume
- 1
- Issue
- 3
- Pages
- 158-168
- ISSN
- 2959-6300 (ISSN-L)
- 2959-6319
- OpenAlex
- W4391523346
- Cited by
- 33
- References
- 0
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 5
- FWCI
- 7.75
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Education Science and Management (publishedVersion, cc-by)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, mathematics achievement, student interest in mathematics, moderating effect, generative AI in education, mathematics education, technology-enhanced learning, undergraduate and graduate students
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