Innovative Teaching Methods Supported by Artificial Intelligence and Students’ Mathematical Problem-Solving: The Mediating Role of Student Engagement
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The study investigated how innovative, AI-supported teaching methods relate to the mathematics problem-solving ability of Senior High School (SHS) students in Ghana. This study examined how teachers’ AI-supported pedagogical practices relate to students’ problem-solving ability, the extent to which student engagement serves as an explanatory variable for that relationship, and whether students’ mathematical self-belief (MSB) moderates that relationship. A quantitative cross-sectional design was employed; participants comprised 385 students from both public and private SHS. Data were collected from a structured questionnaire and analysed with structural equation modeling (SEM). The study results show that AI-supported pedagogical practices of teachers significantly enhance both student engagement in mathematics and problem-solving ability. Student engagement partially mediates the relationship between instructional practices and problem-solving ability, underscoring engagement as a critical mechanism through which effective teaching methods shape learning outcomes. MSB has a meaningful direct influence on problem-solving ability, but does not significantly modify the relationship between pedagogical practices and problem-solving ability. The study emphasises the added value of integrating AI-enhanced teaching methods with effective pedagogical principles to enhance instructional effectiveness and students’ higher-order math skills in secondary education.
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Akma, A. U., Asrizal, A., & Lufri, L. (2025). Structural equation modeling of the contextual teaching-digital learning framework: Pre-dicting 21st century skills development in pre-service elementary teachers. International Journal of Innovative Research and Scientific Studies, 8(6), 1941-1949. https://doi.org/10.53894/ijirss.v8i6.10047
Arthur, Y. D., Appiah, S. K., Amo-Asante, K., & Asare, B. (2022). Modeling student’s interest in mathematics: Role of history of mathematics, peer-assisted learning, and student’s perception. Eurasia Journal of Mathematics, Science and Technology Education, 18(10), em2168. https://doi.org/10.29333/ejmste/12458
Asare, B., & Boateng, F. O. (2025). Self-awareness and self-regulatory learning as mediators between ChatGPT usage and pre-service mathematics teacher’s self-efficacy. Journal of Pedagogical Research, 9(2), 38-54. https://doi.org/10.33902/JPR.202530637
Asare, B., Dissou Arthur, Y., & Adu Obeng, B. (2025). Mathematics self-belief and mathematical creativity of university students: the role of problem-solving skills. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2456438
Asiedu Menlah, C. K., & Boateng, F. O. (2025). Examining the effect of AI-based tutoring systems on students' mathematical prob-lem-solving skills: The moderating role of mathematics anxiety. Journal of Pedagogical Sociology and Psychology, 7(3), 5-17. https://doi.org/10.33902/jpsp.202536137
Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373. https://doi.org/10.1521/jscp.1986.4.3.359
Barfi, K. A., Bervell, B., & Arkorful, V. (2021). Integration of social media for smart pedagogy: initial perceptions of senior high school students in Ghana. Education and Information Technologies, 26(3), 3033-3055. https://doi.org/10.1007/s10639-020-10405-y
Bergdahl, N., Nouri, J., & Fors, U. (2020). Disengagement, engagement and digital skills in technology-enhanced learning. Education and Information Technologies, 25, 957-983. https://doi.org/10.1007/s10639-019-09998-w
Boadu, S. K., & Boateng, F. O. (2024). Enhancing students’ achievement in mathematics education in the 21st century through technology integration, collaborative learning, and student motivation: The mediating role of student interest. Eurasia Journal of Mathematics, Science and Technology Education, 20(11), em2534. https://doi.org/10.29333/ejmste/15622
Boateng, F. O., Bandoh, S. O., Kwarteng, S., & Lotey, E. K. (2024). Impact of Parent Interest in Mathematics and Students Mathematics Interest on Student Mathematics Achievement. Mathematics Education Journal, 8(2), 206-220. https://doi.org/10.22219/mej.v8i2.33182
Cheung, A. C. K., & Slavin, R. E. (2013). The effectiveness of educational technology applications for enhancing mathematics achievement in K-12 classrooms: A meta-analysis. Educational Research Review, 9, 88-113. https://doi.org/10.1016/j.edurev.2013.01.001
Davor, I., Boateng, F. O., & Lotey, E. K. (2025). The mediating role of metacognitive thinking in the relationship between attributional beliefs, academic buoyancy, error-management mindset, and mathematics achievement. European Journal of Mathematics and Science Education, 6(4), 239-253. https://doi.org/10.12973/ejmse.6.4.239
Engelbrecht, J., & Borba, M. C. (2024). Recent developments in using digital technology in mathematics education. ZDM – Mathematics Education, 56(2), 281-292. https://doi.org/10.1007/s11858-023-01530-2
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059
Fredricks, J. A., Reschly, A. L., & Christenson, S. L. (2019). Interventions for student engagement: Overview and state of the field. In J. A. Fredricks, A. L. Reschly, & S. L. Christenson (Eds.), Handbook of student engagement interventions: Working with disengaged students (pp. 1-11). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-813413-9.00001-2
Fung, F., Tan, C. Y., & Chen, G. (2018). Student engagement and mathematics achievement: Unraveling main and interactive effects. Psychology in the Schools, 55(7), 815-831. https://doi.org/10.1002/pits.22139
Garrison, D. R., Cleveland-Innes, M., & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework. The Internet and Higher Education, 13(1-2), 31-36. https://doi.org/10.1016/j.iheduc.2009.10.002
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414-433. https://doi.org/10.1007/s11747-011-0261-6
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alterna-tives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Jeong, J. S., & González-Gómez, D. (2022). Mathematics self-belief comparison and examination of pre-service teacher (PST) through a flipped-open calculation based on numbers (ABN) learning method. Heliyon, 8(7), e09806. https://doi.org/10.1016/j.heliyon.2022.e09806
Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling. Guilford Publications.
Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607-610. https://doi.org/10.1177/001316447003000308
Kumar, S., Gupta, U., Singh, A. K., & Singh, A. K. (2023). Artificial Intelligence: Revolutionizing Cyber Security in the Digital Era. Journal of Computers, Mechanical and Management, 2(3), 31-42. https://doi.org/10.57159/gadl.jcmm.2.3.23064
Lai, C., & Hwang, G. (2016). A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Computers & Education, 100, 126-140. https://doi.org/10.1016/j.compedu.2016.05.006
Li, L., Tong, Y., Wei, L., & Yang, S. (2022). Digital technology-enabled dynamic capabilities and their impacts on firm performance: Evidence from the COVID-19 pandemic. Information & Management, 59(8), 103689. https://doi.org/10.1016/j.im.2022.103689
Marsh, H. W., Guo, J., Dicke, T., Parker, P. D., & Craven, R. G. (2020). Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony. Multivariate Behavioral Research, 55(1), 102-119. https://doi.org/10.1080/00273171.2019.1602503
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of Method Bias in Social Science Research and Recommendations on How to Control It. Annual Review of Psychology, 63, 539-569. https://doi.org/10.1146/annurev-psych-120710-100452
Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Monés, A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28, 6695-6726. https://doi.org/10.1007/s10639-022-11431-8
Tondeur, J., van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: a systematic review of qualitative evidence. Educational Technology Research and Development, 65, 555-575. https://doi.org/10.1007/s11423-016-9481-2
Usher, E. L., & Pajares, F. (2007). Self-Efficacy for Self-Regulated Learning: A Validation Study: A Validation Study. Educational and Psychological Measurement, 68(3), 443-463. https://doi.org/10.1177/0013164407308475
Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51, 409-428. https://doi.org/10.3758/s13428-018-1055-2
Yin, X., Bin Mohd Saad, M. R., & Binti Abdul Halim, H. (2024). Technology-enhanced social learning (TSL) to foster critical thinking dispositions and thinking in writing. Cogent Education, 11(1). https://doi.org/10.1080/2331186X.2024.2341584