Abstract
Artificial intelligence (AI) is increasingly being integrated into higher education; however, most instructional applications emphasizeanswer generation rather than mathematical reasoning and critical verification of AI-generated mathematical solutions. The current study examined the effectiveness of the Guided AI Integration Model (GAIIM), a verification-oriented instructional framework that utilizes AI for verification of students’ algebraic problem-solving within a problem-solving context. An explanatory sequential mixed-methods design was employed. Quantitative data were collected from 86 undergraduate mathematics education students using a randomized pretest-posttest control group classroom design. During the 12-week intervention, students in the experimental group engaged in GAIIM-based instruction involving independent problem-solving, AI-generated solution comparison, error detection and justification, error resolution, and reflective verification, while the control group received conventional algebra instruction. Qualitative data were subsequently collected from 10 purposively selected students through semi-structured interviews and analyzed thematically to explain and elaborate on the quantitative findings. Students in the GAIIM group demonstrated significantly higher posttest performance in algebraic problem-solving, error detection, and error resolution after controlling for pretest scores. Qualitative findings further revealed stepwise verification, iterative engagement with AI feedback, conditional trust in AI-generated responses, and difficulties interpreting and correcting AI-generated mathematical reasoning. The findings suggest that structured AI-supported verification can support students’ algebraic problem-solving and error-verification performance.
- Ahzan, Z. N., Simarmata, J. E., & Mone, F. (2022). Using Newman error analysis to detect students’ error in solving junior high school mathematics problem. Jurnal Pendidikan MIPA, 23(2), 459-473. https://doi.org/10.23960/jpmipa/v23i2.pp459-473
- Akendita, P. A., Boateng, F. O., Arthur, Y. D., & Banson, G. M. (2025). Investigating the effect of perception of artificial intelligence on pre-service teachers’ mathematics performance in Ghana: the moderating role of interest. Artificial Intelligence in Education, 1(1), 93–113. https://doi.org/10.1108/AIIE-09-2024-0004
- Asamoah, M. K., & Amarteifio, J. (2025). Overcoming barriers and ethical challenges in implementing intelligent tutoring systems in Ghanaian higher education. Discover Education, 4(1), 179. https://doi.org/10.1007/s44217-025-00593-5
- Atuahene, E., & Boateng, F. O. (2026). Mathematics teachers’ awareness, perceptions, and challenges in using ChatGPT. Discover Education, 5(1), 88. https://doi.org/10.1007/s44217-025-01083-4
- Baumgartner, V., Daguati, S., Trninic, D., Akveld, M., Caspar, A., Hungerbühler, N., & Kapur, M. (2025). Problem-solving before instruction for learning linear algebra in university mathematics. Instructional Science, 53(6), 1573–1602. https://doi.org/10.1007/s11251-025-09709-8
- Boateng, O. F., Davor, I., & Appiah Manu, C. (2026). Innovative teaching methods supported by artificial intelligence and students’ mathematical problem-solving: the mediating role of student engagement. EIKI Journal of Effective Teaching Methods, 4(1). https://doi.org/10.59652/7tsj5730
- Boateng, F. O., Davor, I., Larbi, E., & Boateng, E. A. (2026). Scaffolded and unscaffolded generative AI use in mathematics teacher education: Effects on pre-service teachers’ instructional design competence [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-10778501/v1
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
- Campanilla, N. S., & Mendoza, C. R. (2024). Error pattern analysis of the mathematics problem solving of grade 10 learners. Journal of Social, Humanity, and Education, 4(4), 245-262. https://doi.org/10.35912/jshe.v4i4.2125
- Cevikbas, M., & Kaiser, G. (2026). The use of large language models to solve mathematical modeling problems: Preservice mathematics teachers’ use practices, perceived affordances and challenges, and trustworthiness judgments of AI-generated outputs. ZDM–Mathematics Education, 1-17. https://doi.org/10.1007/s11858-026-01786-4
- Contel, F., & Cusi, A. (2025). Investigating the role of ChatGPT in supporting metacognitive processes during problem-solving activities. Digital Experiences in Mathematics Education, 11(1), 167–191. https://doi.org/10.1007/s40751-024-00164-7
- Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
- Davor, I. (2026). Generative AI-supported instruction as a cognitive scaffold: Effects on senior high school students’ geometric reasoning and proof construction. Frontiers in Education, 11, 1869961. https://doi.org/10.3389/feduc.2026.1869961
- Davor, I., & Boateng, F. O. (2026). AI-enhanced pedagogical practices and mathematical language proficiency in STEM education. American Journal of STEM Education, 22, 237-262.https://doi.org/10.32674/6h0e3376
- Davor, I., Larbi, E., & Boateng, F. O. (2026). Artificial intelligence-supported learning and higher-order cognitive outcomes: The mediating role of metacognitive self-regulation. Frontiers in Education, 11, 1855869. https://doi.org/10.3389/feduc.2026.1855869
- Davor, I., Boateng, F. O., Osei-Tutu, P., Osei-Bosompem, A., Patience, A. S., & Adusei, L. A. (2026). Examining the mediating roles of digital literacy and critical thinking in the relationship between ChatGPT use and university students’ mathematical problem-solving efficacy. Journal of Mathematics, Science and Technology Education, 3(1), 1–15. https://doi.org/10.12973/jmste.3.1.1
- Gimpel, H., Hall, K., Decker, S., Eymann, T., Lämmermann, L., Mädche, A., Röglinger, M., Ruiner, C., Schoch, M., Schoop, M., Urbach, N., & Vandirk, S. (2023). Unlocking the power of generative AI models and systems such as GPT-4 and ChatGPT for higher education: A guide for students and lecturers (Discussion Paper No. 02-2023). University of Hohenheim.
- Gqoli, N., & Baidoo, J. (2025). The integration of artificial intelligence in mathematics education: towards developing a conceptual framework for understanding student perspectives in higher education. E-Journal of Humanities, Arts and Social Sciences, 2660–2674. https://doi.org/10.38159/ehass.202561031
- Habtamu, S. B., Mulugeta, A. A., & Mulugeta, W. G. (2022). The effect of cooperative problem-solving method on students’ motivation towards learning algebra. Pedagogical Research, 7(2), em0123. https://doi.org/10.29333/pr/11906
- Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165
- Joseph, O. B., & Uzondu, N. C. (2024). Integrating AI and machine learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal, 5(8), 1732–1750. https://doi.org/10.51594/csitrj.v5i8.1379
- Julius, E. (2022). The relationship between self-concept and problem-solving skills on students’ attitude towards solving algebraic problems. Contemporary Mathematics and Science Education, 3(2), ep22020. https://doi.org/10.30935/conmaths/12509
- Jupri, A., & Drijvers, P. H. M. (2016). Student difficulties in mathematizing word problems in algebra. Eurasia Journal of Mathematics, Science and Technology Education, 12(9), 2481-2502. https://doi.org/10.12973/eurasia.2016.1299a
- Kazadi Tshikolu, R., Kule Mukuhi, D., Nzalalemba Kabwangala, T., Ntiaka Muzakwene, J., & Sunda-Meya, A. (2025). AI-enhanced problem-based learning for sustainable engineering education: The AIPLE framework for developing countries. Sustainability,17(20), 9038. https://doi.org/10.3390/su17209038
- Kirsten, K., & Greefrath, G. (2023). Proof construction and in-process validation – Validation activities of undergraduates in constructing mathematical proofs. The Journal of Mathematical Behavior, 70, 101064. https://doi.org/10.1016/j.jmathb.2023.101064
- Kusuma, A. P., Waluya, St. B., Rochmad, R., & Mariani, S. (2024). Algebraic thinking profile of pre-service teachers in solving mathematical problems in relation to their self-efficacy. Eurasia Journal of Mathematics, Science and Technology Education, 20(11), em2532. https://doi.org/10.29333/ejmste/15580
- Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative ai for higher education as explained by ChatGPT. Education Sciences, 13(9), 856. https://doi.org/10.3390/educsci13090856
- Özcan, Z. Ç. (2024). Evaluating the impact of the WEI4s instructional approach on middle school students’ algebraic problem-solving skills. Education Sciences, 14(1), 102. https://doi.org/10.3390/educsci14010102
- Pardos, Z. A., & Bhandari, S. (2024). ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills. PLOS ONE, 19(5), e0304013. https://doi.org/10.1371/journal.pone.0304013
- Polotskaia, E., Fellus, O. O., Cavalcante, A., & Savard, A. (2022). Students’ problem solving and transitioning from numerical to relational thinking. Canadian Journal of Science, Mathematics and Technology Education, 22(2), 341–364. https://doi.org/10.1007/s42330-022-00218-1
- Santos-Trigo, M. (2024). Problem solving in mathematics education: Tracing its foundations and current research-practice trends. ZDM–Mathematics Education, 56, 211–222. https://doi.org/10.1007/s11858-024-01578-8
- Shulman, L. S., & Carey, N. B. (1984). Psychology and the limitations of individual rationality: Implications for the study of reasoning and learning. Review of Educational Research, 54(4), 503–530. https://doi.org/10.3102/00346543054004503
- Sivan, E. (1986). Motivation in social constructivist theory. Educational Psychologist, 21(3), 209–233. https://doi.org/10.1207/s15326985ep2103_4
- Sommerhoff, D., Kollar, I., & Ufer, S. (2025). Undergraduate Students’ Mathematical Proof Skills: Examining the Impact of Six Cognitive Resources (pp. 45–71). https://doi.org/10.1007/978-3-031-85004-2_3
- Teresi, J. A., Yu, X., Stewart, A. L., & Hays, R. D. (2022). Guidelines for designing and evaluating feasibility pilot studies. Medical care, 60(1), 95-103. https://doi.org/10.1097/MLR.0000000000001664
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
- Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
- Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9, Article 59. https://doi.org/10.1186/s40594-022-00377-5
- Yelamarthi, K., Dandu, R., Rao, M., Yanambaka, V. P., & Mahajan, S. (2024). Exploring the potential of generative AI in shaping engineering education: Opportunities and challenges. Journal of Engineering Education Transformations, 37(Special Issue 2), 439–445. https://doi.org/10.16920/jeet/2024/v37is2/24072
- Zacharis, G., & Papadakis, S. (2025). Can AI grade like a human? Validity, reliability, and fairness in university coursework assessment. Educational Process: International Journal, 19, e2025591. https://doi.org/10.22521/edupij.2025.19.591
APA 7th edition
In-text citation: (Davor et al., 2026)
Reference: Davor, I., Larbi, E., Boateng, F. O., & Tawiah, M. O. (2026). Guided AI integration model (GAIIM): Effects of verification-oriented generative AI-supported learning on undergraduate students’ algebraic problem-solving.
Journal of Interdisciplinary Research in Artificial Intelligence and Society, 2(1), Article 8.
https://doi.org/10.20897/jirais/19556
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Davor I, Larbi E, Boateng FO, Tawiah MO. Guided AI integration model (GAIIM): Effects of verification-oriented generative AI-supported learning on undergraduate students’ algebraic problem-solving.
Journal of Interdisciplinary Research in Artificial Intelligence and Society. 2026;2(1), 8.
https://doi.org/10.20897/jirais/19556
Chicago
In-text citation: (Davor et al., 2026)
Reference: Davor, Isaac, Ernest Larbi, Francis Ohene Boateng, and Michael Osei Tawiah. "Guided AI integration model (GAIIM): Effects of verification-oriented generative AI-supported learning on undergraduate students’ algebraic problem-solving".
Journal of Interdisciplinary Research in Artificial Intelligence and Society 2026 2 no. 1 (2026): 8.
https://doi.org/10.20897/jirais/19556
Harvard
In-text citation: (Davor et al., 2026)
Reference: Davor, I., Larbi, E., Boateng, F. O., and Tawiah, M. O. (2026). Guided AI integration model (GAIIM): Effects of verification-oriented generative AI-supported learning on undergraduate students’ algebraic problem-solving.
Journal of Interdisciplinary Research in Artificial Intelligence and Society, 2(1), 8.
https://doi.org/10.20897/jirais/19556
MLA
In-text citation: (Davor et al., 2026)
Reference: Davor, Isaac et al. "Guided AI integration model (GAIIM): Effects of verification-oriented generative AI-supported learning on undergraduate students’ algebraic problem-solving".
Journal of Interdisciplinary Research in Artificial Intelligence and Society, vol. 2, no. 1, 2026, 8.
https://doi.org/10.20897/jirais/19556
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Davor I, Larbi E, Boateng FO, Tawiah MO. Guided AI integration model (GAIIM): Effects of verification-oriented generative AI-supported learning on undergraduate students’ algebraic problem-solving. Journal of Interdisciplinary Research in Artificial Intelligence and Society. 2026;2(1):8.
https://doi.org/10.20897/jirais/19556