Comparing Gen AI adoption in pre- and in-service mathematics teachers

Phuong Bui; Mirela Vinerean-Bernhoff; Yvonne Liljekvist

doi:10.31129/LUMAT.14.1.2983

Authors

Phuong Bui Faculty of Health, Natural Sciences and Engineering, Department of Mathematics and Computer Science, Karlstad University, Sweden https://orcid.org/0000-0001-9278-0515
Mirela Vinerean-Bernhoff Faculty of Health, Natural Sciences and Engineering, Department of Mathematics and Computer Science, Karlstad University, Sweden https://orcid.org/0000-0001-7825-0243
Yvonne Liljekvist Faculty of Health, Natural Sciences and Engineering, Department of Mathematics and Computer Science, Karlstad University, Sweden https://orcid.org/0000-0002-7956-8795

DOI:

https://doi.org/10.31129/LUMAT.14.1.2983

Keywords:

generative AI, teacher education, mathematics education, Gen AI adoption, Gen AI readiness

Abstract

This study examines Generative Artificial Intelligence (Gen AI) integration in a Swedish mathematics education context by investigating readiness, usage patterns, and approaches among pre-service (N=17) and in-service (N=22) mathematics teachers. Using a mixed-methods approach within a teacher training program in spring 2025, the research addresses three questions: (1) current Gen AI readiness levels, behavioral intentions, and attitudes; (2) differences between groups in integration approaches; and (3) practical applications, challenges, and coping strategies. Data collection included quantitative readiness measures and qualitative responses about experiences. Results revealed that both career stage and usage frequency influence Gen AI adoption: career stage shapes the qualitative nature of integration (purposes, approaches, sophistication), while usage frequency emerges as a stronger predictor of learning changes and readiness levels. Pre-service teachers demonstrated higher usage for learning, while in-service teachers showed selective, professionally-informed usage despite similar readiness levels. High-frequency users reported significantly greater learning changes and higher readiness scores, highlighting hands-on experience’s role in developing AI competencies. The study identifies two tentative patterns: pre-service teachers’ developmental integration as learning scaffolds versus in-service teachers’ selective, professionally-informed integration with more sophisticated strategies. These exploratory findings offer preliminary evidence of how mathematics educators at different career stages conceptualize Gen AI integration, and point to directions for future research, targeted professional development and teacher training programs.

References

Alhazzani, N. S. (2024). Enhancing mathematics teachers’ pedagogical skills by using ChatGPT. International Journal of Innovative Research and Scientific Studies, 7(4), 1614–1626. https://doi.org/10.53894/ijirss.v7i4.3460

American Association of Colleges and Universities. (2025). Higher Education Leaders Navigate AI Disruption:New Survey Highlights Challenges and Opportunities in Teaching and Learning [Press release]. https://www.aacu.org/newsroom/higher-education-leaders-navigate-ai-disruption

Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099

Bergdahl, N., & Sjöberg, J. (2025). Attitudes, perceptions and AI self-efficacy in K-12 education. Computers and Education: Artificial Intelligence, 8, 100358. https://doi.org/10.1016/j.caeai.2024.100358

Biton, Y., & Segal, R. (2025). Learning to Craft and Critically Evaluate Prompts: The Role of Generative AI (ChatGPT) in Enhancing Pre-service Mathematics Teachers’ TPACK and Problem-Posing Skills. International Journal of Education in Mathematics, Science and Technology, 13(1), 202–223. https://doi.org/10.46328/ijemst.4654

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., … Liang, P. (2021). On the Opportunities and Risks of Foundation Models (Version 3). arXiv. https://doi.org/10.48550/ARXIV.2108.07258

Botana, F., & Recio, T. (2024). Geometric Loci and ChatGPT: Caveat Emptor! Computation, 12(2), 30. https://doi.org/10.3390/computation12020030

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Brislin, R. W. (1970). Back-Translation for Cross-Cultural Research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301

Bui, P., Pongsakdi, N., McMullen, J., & Veermans, M. (2025a). The Rapidly Evolving Landscape of ChatGPT in Mathematics Education: A Comprehensive Scoping Review (2023-2025). Open Science Framework. https://doi.org/10.31219/osf.io/ce2ky_v2

Bui, P., Korhonen, T., Kontkanen, S., Karme, S., Piispa-Hakala, S., & Veermans, M. (2025b). Exploring pre-service teachers’ generative AI readiness and behavioral intentions: A pilot study. LUMAT: International Journal on Math, Science and Technology Education, 13(1), 8. https://doi.org/10.31129/LUMAT.13.1.2755

Busuttil, L., & Calleja, J. (2025). Teachers’ Beliefs and Practices About the Potential of ChatGPT in Teaching Mathematics in Secondary Schools. Digital Experiences in Mathematics Education, 11(1), 140–166. https://doi.org/10.1007/s40751-024-00168-3

Chatfield, T. (2025). AI and the Future of Pedagogy. Sage. https://doi.org/10.4135/wp520172

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

Culp-Roche, A., Hampton, D., Hensley, A., Wilson, J., Thaxton-Wiggins, A., Otts, J. A., Fruh, S., & Moser, D. K. (2020). Generational Differences in Faculty and Student Comfort With Technology Use. SAGE Open Nursing, 6, 2377960820941394. https://doi.org/10.1177/2377960820941394

Dahal, N., Lamichhnae, B., Luitel, B. C., & Pant, B. P. (2023). AI chatbots as math algorithm problem solvers: A critical evaluation of its capabilities and limitations. In Proceedings of the 28th Asian Technology Conference in Mathematics (Vol. 28, No. 1, pp. 429-438).

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Ergene, O., & Ergene, B. C. (2025). AI ChatBots’ solutions to mathematical problems in interactive e-textbooks: Affordances and constraints from the eyes of students and teachers. Education and Information Technologies, 30(1), 509–545. https://doi.org/10.1007/s10639-024-13121-z

Field, A. (2024). Discovering statistics using IBM SPSS statistics. Sage publications limited.

Freeman, J. (2025). Student Generative AI Survey 2025 (HEPI Policy Note No. 61). Higher Education Policy Institute. https://www.hepi.ac.uk/wp-content/uploads/2025/02/HEPI-Kortext-Student-Generative-AI-Survey-2025.pdf

Getenet, S. (2024). Pre-service teachers and ChatGPT in multistrategy problem-solving: Implications for mathematics teaching in primary schools. International Electronic Journal of Mathematics Education, 19(1), em0766. https://doi.org/10.29333/iejme/14141

Granström, M., & Oppi, P. (2025). Assessing teachers’ readiness and perceived usefulness of AI in education: An Estonian perspective. Frontiers in Education, 10, 1622240. https://doi.org/10.3389/feduc.2025.1622240

Gurl, T. J., Markinson, M. P., & Artzt, A. F. (2025). Using ChatGPT as a Lesson Planning Assistant with Preservice Secondary Mathematics Teachers. Digital Experiences in Mathematics Education, 11(1), 114–139. https://doi.org/10.1007/s40751-024-00162-9

Kim, Y. R., Park, M. S., & Joung, E. (2025). Exploring the integration of artificial intelligence in math education: Preservice Teachers’ experiences and reflections on problem‐posing activities with ChatGPT. School Science and Mathematics, ssm.18336. https://doi.org/10.1111/ssm.18336

Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.

Lang, S. (2023). Learning Management Systems (LMSs). In O. Kruse, C. Rapp, C. M. Anson, K. Benetos, E. Cotos, A. Devitt, & A. Shibani (Eds), Digital Writing Technologies in Higher Education (pp. 173–182). Springer International Publishing. https://doi.org/10.1007/978-3-031-36033-6_11

Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, 100174. https://doi.org/10.1016/j.caeo.2024.100174

Lee, J., Soylu, M. Y., & Ou, C. (2023). Exploring insights from online students: Enhancing the design and development of intelligent textbooks for the future of online education. International Journal on Innovations in Online Education, 7(2), 29–55. https://doi.org/10.1615/IntJInnovOnlineEdu.2023049742

Leh, A. S. (2005). Lessons learned from service learning and reverse mentoring in faculty development: A case study in technology training. Journal of Technology and Teacher Education, 13(1), 25–41.

Li, P.-H., Lee, H.-Y., Cheng, Y.-P., Starčič, A. I., & Huang, Y.-M. (2023). Solving the Self-regulated Learning Problem: Exploring the Performance of ChatGPT in Mathematics. In Y.-M. Huang & T. Rocha (Eds), Innovative Technologies and Learning (Vol. 14099, pp. 77–86). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40113-8_8

Liu, Y., & Wang, H. (2026). Who on Earth Is Using Generative AI?. World Development, 199, 107260. https://doi.org/10.1016/j.worlddev.2025.107260

Mai, D. T. T., Da, C. V., & Hanh, N. V. (2024). The use of ChatGPT in teaching and learning: A systematic review through SWOT analysis approach. Frontiers in Education, 9, 1328769. https://doi.org/10.3389/feduc.2024.1328769

Mishra, P., & Koehler, M. J. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record: The Voice of Scholarship in Education, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x

Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and Generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235–251. https://doi.org/10.1080/21532974.2023.2247480

Moorhouse, B. L. (2024). Beginning and first-year language teachers’ readiness for the generative AI age. Computers and Education: Artificial Intelligence, 6, 100201. https://doi.org/10.1016/j.caeai.2024.100201

National Council of Teachers of Mathematics. (2024). Artificial intelligence and mathematics teaching: A position of the National Council of Teachers of Mathematics. Retrieved from https://www.nctm.org/uploadedFiles/Standards_and_Positions/Position_Statements/NCTM_AI_Position_Statement.pdf

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Niess, M. L. (2005). Preparing teachers to teach science and mathematics with technology: Developing a technology pedagogical content knowledge. Teaching and Teacher Education, 21(5), 509–523. https://doi.org/10.1016/j.tate.2005.03.006

Niess, M. L. (2011). Investigating TPACK: Knowledge Growth in Teaching with Technology. Journal of Educational Computing Research, 44(3), 299–317. https://doi.org/10.2190/EC.44.3.c

Noster, N., Gerber, S., & Siller, H.-S. (2024). Pre-Service Teachers’ Approaches in Solving Mathematics Tasks with ChatGPT. Digital Experiences in Mathematics Education, 10(3), 543–567. https://doi.org/10.1007/s40751-024-00155-8

Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A Systematic Review of Generative AI for Teaching and Learning Practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636

OpenAI. (2024). GPT-4. https://openai.com/index/gpt-4-research/

Ozdemir, N., & Mede, E. (2024). Exploring In-service EFL Teachers’ Readiness for the Generative AI Age. International Journal of Research in Teacher Education, 15(5).

Parasuraman, A., & Colby, C. L. (2015). An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730

Pegler, K., Kollewyn, J., & CriChton, S. (2010). Generational attitudes and teacher ICT use. Journal of Technology and Teacher Education, 18(3), 443–458.

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

Petko, D., Mishra, P., & Koehler, M. J. (2025). TPACK in context: An updated model. Computers and Education Open, 8, 100244. https://doi.org/10.1016/j.caeo.2025.100244

Pettersson, J., Hult, E., Eriksson, T., & Adewumi, O. (2024). Generative AI and Teachers - For Us or Against Us? A Case Study. 37–43. https://doi.org/10.3384/ecp208005

Priyanda, R., Herman, T., Amalia, R., & Ihsan, I. R. (2025). Exploring teachers’ pedagogical reasoning in mathematics education using the TPACK framework. Frontiers in Education, 10, 1552760. https://doi.org/10.3389/feduc.2025.1552760

Sayeg-Sánchez, G., & Rodriguez-Paz, M. X. (2024). Use of Game-Based Learning with ChatGPT to Improve Mathematical Modeling Competences in First-Year Engineering Students. In Proceedings of the ASEE Annual Conference & Exposition. https://doi.org/10.18260/1-2--48216

Schellaert, W., Martínez-Plumed, F., Vold, K., Burden, J., A. M. Casares, P., Sheng Loe, B., Reichart, R., Ó hÉigeartaigh, S., Korhonen, A., & Hernández-Orallo, J. (2023). Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models. Journal of Artificial Intelligence Research, 77, 377–394. https://doi.org/10.1613/jair.1.14157

Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics (Reprint). Journal of Education, 196(2), 1–38. https://doi.org/10.1177/002205741619600202

Skolverket. (2024). Advice on AI, Chatbots and similar tools. https://www.skolverket.se/skolutveckling/inspiration-och-stod-i-arbetet/stod-i-arbetet/rad-om-ai-chattbottar-och-liknande-verktyg

Söderström, U., Hedström, E., Lambertsson, K., & Mejtoft, T. (2024). ChatGPT in education: Teachers’ and Students’ views. Proceedings of the European Conference on Cognitive Ergonomics 2024, 1–10. https://doi.org/10.1145/3673805.3673828

Stryker, C., & Scapicchio, M. (2024, March 22). What is Generative AI? Https://Www.ibm.com. https://www.ibm.com/topics/generative-ai

Taani, O., & Alabidi, S. (2024). ChatGPT in education: Benefits and challenges of ChatGPT for mathematics and science teaching practices. International Journal of Mathematical Education in Science and Technology, 1–30. https://doi.org/10.1080/0020739X.2024.2357341

Tankelevitch, L., Kewenig, V., Simkute, A., Scott, A. E., Sarkar, A., Sellen, A., & Rintel, S. (2024). The Metacognitive Demands and Opportunities of Generative AI. Proceedings of the CHI Conference on Human Factors in Computing Systems, 1–24. https://doi.org/10.1145/3613904.3642902

Trouche, L. (2005). Calculators in Mathematics Education: A Rapid Evolution of Tools, with Differential Effects. In D. Guin, K. Ruthven, & L. Trouche (Eds), The Didactical Challenge of Symbolic Calculators (Vol. 36, pp. 9–39). Springer-Verlag. https://doi.org/10.1007/0-387-23435-7_2

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272

Yoon, H., Hwang, J., Lee, K., Roh, K. H., & Kwon, O. N. (2024). Students’ use of generative artificial intelligence for proving mathematical statements. ZDM – Mathematics Education, 56(7), 1531–1551. https://doi.org/10.1007/s11858-024-01629-0

Yunianto, W., Lavicza, Z., Kastner-Hauler, O., & Houghton, T. (2024). Investigating the use of ChatGPT to solve a GeoGebra based mathematics+computational thinking task in a geometry topic. Journal on Mathematics Education, 15(3), 1027–1052. https://doi.org/10.22342/jme.v15i3.pp1027-1052

Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7