Unlocking AI acceptance among pre-service mathematics teachers: development and validation of a TAM-based instrument

Annisa Dwi Kurniawati; Mega Melinda Ayu Pratiwi

doi:10.32332/linear.v7i1.65-81

Authors

Annisa Dwi Kurniawati Universitas Islam Negeri Kiai Ageng Muhammad Besari Ponorogo
Mega Melinda Ayu Pratiwi Universitas Islam Negeri Kiai Ageng Muhammad Besari Ponorogo

DOI:

https://doi.org/10.32332/linear.v7i1.65-81

Keywords:

Artificial intelligence in education, Technology Acceptance Model, mathematics teacher education, transformative learning, instrument development

Abstract

The rapid integration of artificial intelligence (AI) in mathematics education has shifted instructional practices beyond efficiency toward more transformative learning experiences. However, the successful adoption of AI depends largely on pre-service teachers' acceptance of these technologies. This study aimed to develop and validate a Technology Acceptance Model (TAM)–based instrument to measure pre-service mathematics teachers' acceptance of AI in transformative learning contexts. A research and development design employing the 4D model (Define, Design, Develop, Disseminate) was implemented. The instrument focused on four core TAM constructs: Perceived Usefulness of AI (PU-AI), Perceived Ease of Use of AI (PEOU-AI), Attitude Toward Use of AI (ATU-AI), and Behavioural Intention to Use AI (BI-AI), with items contextualised for mathematics learning and transformative pedagogical practices. Content validity was evaluated by two expert validators using the Gregory content validity coefficient, and interrater reliability was assessed to ensure consistency across expert evaluations. The results indicated high content validity (V = 0.90) and strong inter-rater agreement (80%), supporting the adequacy and clarity of the developed items. A pilot test involving 47 pre-service mathematics teachers was conducted to examine the clarity and usability of the instrument. Based on expert feedback and pilot testing, a final instrument comprising 20 items was produced, with each item representing a distinct indicator across the four constructs. Based on the results, this instrument is a potentially context-sensitive tool for assessing AI acceptance among pre-service mathematics teachers. These initial findings may inform future research and evaluation efforts, though further psychometric testing, including internal reliability, construct validity, and factor analysis, is recommended before broader application in AI-supported mathematics education.

References

Abbott, A., Shin, J., Carlson, K., Russell, M., Qi, Y., Storm, H., & Jewell, V. (2024). Achieving Inter-Rater Agreement and Inter-Rater Reliability to Assess Fidelity of an Occupation-Based Coaching (OBC) Clinical Trial Intervention. British Journal of Occupational Therapy, 88, 133–141. https://doi.org/10.1177/03080226241283292

Alejandro, I. M., Sánchez, J. M., Sumalinog, G., Mananay, J., Goles, C., & Fernández, C. (2024). Pre-service teachers' technology acceptance of artificial intelligence (AI) applications in education. STEM Education. https://doi.org/10.3934/steme.2024024

Alqarni, A. (2025). Artificial Intelligence-Critical Pedagogic: Design and Psychologic Validation of a Teacher-Specific Scale for Enhancing Critical Thinking in Classrooms. J. Comput. Assist. Learn., 41. https://doi.org/10.1111/jcal.70039

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. https://doi.org/10.46328/ijemst.4654

Canonigo, A. M. (2024). Levering AI to enhance students’ conceptual understanding and confidence in mathematics. Journal of Computer Assisted Learning, 40(6), 3215–3229. https://doi.org/10.1111/jcal.13065

Dahri, N. A., Yahaya, N., Al-Rahmi, W., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10. https://doi.org/10.1016/j.heliyon.2024.e29317

Deng, R., Benckendorff, P., & Gannaway, D. (2019). Learner engagement in MOOCs: Scale development and validation. Br. J. Educ. Technol., 51, 245–262. https://doi.org/10.1111/bjet.12810

Gabriel, F., Kennedy, J., Marrone, R., & Leonard, S. (2025). Pragmatic AI in education and its role in mathematics learning and teaching. NPJ Science of Learning, 10. https://doi.org/10.1038/s41539-025-00315-4

Gümüş, M. M., & Kukul, V. (2022). Developing a digital competence scale for teachers: Validity and reliability study. Education and Information Technologies, 28, 2747–2765. https://doi.org/10.1007/s10639-022-11213-2

Guo, S., Shi, L., & Zhai, X. (2025). Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education. Education and Information Technologies, 30, 13439–13461. https://doi.org/10.1007/s10639-025-13338-6

Gupta, S., Grover, S., Menon, V., Indu, P., Chacko, D., & S, M. G. (2025). Item generation and establishing face and content validity of a rating scale: A primer. Indian Journal of Psychiatry, 67, 816–822. https://doi.org/10.4103/indianjpsychiatry_750_25

Gurer, M. D. (2021). Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach. Education and Information Technologies, 26, 4709–4729. https://doi.org/10.1007/s10639-021-10493-4

Gustilo, L., Ong, E., & Lapinid, M. (2024). Algorithmically-driven writing and academic integrity: Exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity, 20, 1–43. https://doi.org/10.1007/s40979-024-00153-8

Joly, B., Gray, C., Cousineau, K., Pearson, K., & Harder, V. (2025). The development, validity, and reliability of the Researcher Investment Tool. Journal of Clinical and Translational Science, 9. https://doi.org/10.1017/cts.2024.673

Li, W., Zhang, X., Li, J., Yang, X., Li, D., & Liu, Y. (2024). An explanatory study of factors influencing engagement in AI education at the K-12 Level: An extension of the classic TAM model. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-64363-3

McHugh, M. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22, 276–282. https://doi.org/10.11613/bm.2012.031

O’Neill, T. (2017). An Overview of Interrater Agreement on Likert Scales for Researchers and Practitioners. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00777

Prieto, J. C. S., Cruz-Benito, J., Therón, R., & García-Peñalvo, F. (2020). Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students. Int. J. Interact. Multim. Artif. Intell., 6, 80–86. https://doi.org/10.9781/ijimai.2020.11.009

Pueyo‐Garrigues, M., Pardavila‐Belio, M., Whitehead, D., Esandi, N., Canga‐Armayor, A., Elosua, P., & Canga‐Armayor, N. (2020). Nurses’ knowledge, skills and personal attributes for competent health education practice: An instrument development and psychometric validation study. Journal of Advanced Nursing. https://doi.org/10.1111/jan.14632

Qin, L., Ho, W. K. Y., & Khoo, S. (2025). Development and validation of a measurement instrument for student assessment of quality physical education in Chinese secondary schools. PLOS One, 20. https://doi.org/10.1371/journal.pone.0324227

Runge, I., Hebibi, F., & Lazarides, R. (2025). Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model. Education Sciences. https://doi.org/10.3390/educsci15020167

Setiawan, R., Wagiran, W., & Alsamiri, Y. (2024). Construction of an instrument for evaluating the teaching process in higher education: Content and construct validity. REID (Research and Evaluation in Education). https://doi.org/10.21831/reid.v10i1.63483

Shweta, Bajpai, R., & Chaturvedi, H. (2015). Evaluation of Inter-Rater Agreement and Inter-Rater Reliability for Observational Data: An Overview of Concepts and Methods. Journal of the Indian Academy of Applied Psychology, 41, 20.

Song, X., Mak, J., & Chen, H. (2025). Teachers and Learners’ Perceptions about Implementation of AI Tools in Elementary Mathematics Classes. SAGE Open, 15(2), 21582440251334545. https://doi.org/10.1177/21582440251334545

Stefana, A., Damiani, S., Granziol, U., Provenzani, U., Solmi, M., Youngstrom, E., & Fusar-Poli, P. (2025). Psychological, psychiatric, and behavioral sciences measurement scales: Best practice guidelines for their development and validation. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1494261

Stefanova, T., & Georgiev, S. (2024). Possibilities for using AI in mathematics education. Mathematics and Education in Mathematics. https://doi.org/10.55630/mem.2024.53.117-125

Van Den Berg, S., & Papadopoulos, P. (2024). Summative assessment with artificial intelligence: Qualitative analysis and comparison of technology acceptance in student and teacher populations. Innovations in Education and Teaching International, 62, 1529–1544. https://doi.org/10.1080/14703297.2024.2436613

Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, 1–29. https://doi.org/10.1186/s41239-024-00448-3

Wang, Y., Wei, Z., Wijaya, T. T., Cao, Y., & Ning, Y. (2025). Awareness, acceptance, and adoption of Gen-AI by K-12 mathematics teachers: An empirical study integrating TAM and TPB. BMC Psychology, 13. https://doi.org/10.1186/s40359-025-02781-2

Westergård, E., Ertesvåg, S., & Rafaelsen, F. (2019). A Preliminary Validity of the Classroom Assessment Scoring System in Norwegian Lower-Secondary Schools. Scandinavian Journal of Educational Research, 63, 566–584. https://doi.org/10.1080/00313831.2017.1415964

Wilhelm, A., Rouse, A., & Jones, F. (2018). Exploring Differences in Measurement and Reporting of Classroom Observation Inter-Rater Reliability. Practical Assessment, Research and Evaluation, 23, 4.

Wongpakaran, N., Wongpakaran, T., Pinyopornpanish, M., Simcharoen, S., Suradom, C., Varnado, P., & Kuntawong, P. (2020). Development and validation of a 6-item Revised UCLA Loneliness Scale (RULS-6) using Rasch analysis. British Journal of Health Psychology. https://doi.org/10.1111/bjhp.12404

Xu, Z. (2024). AI in education: Enhancing learning experiences and student outcomes. Applied and Computational Engineering. https://doi.org/10.54254/2755-2721/51/20241187

Yi, L., Liu, D., Jiang, T., & Xian, Y. (2024). The Effectiveness of AI on K-12 Students’ Mathematics Learning: A Systematic Review and Meta-Analysis. International Journal of Science and Mathematics Education, 23, 1105–1126. https://doi.org/10.1007/s10763-024-10499-7

Yılmaz, Z., Galanti, T., Naresh, N., & Kanbir, S. (2025). Exploring the interactions among instructor, prospective teachers and AI in facilitating mathematics learning. School Science and Mathematics. https://doi.org/10.1111/ssm.18341

Zhang, C., Hu, M., Wu, W., Chen, Y., Kamran, F., & Wang, X. (2025). A profile analysis of pre-service teachers’ AI acceptance: Combining behavioral, technological, and human factors. Teaching and Teacher Education. https://doi.org/10.1016/j.tate.2025.105086

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. https://doi.org/10.1186/s41239-023-00420-7

Zhao, X., Feng, G., Ao, S., & Liu, P. L. (2022). Interrater reliability estimators tested against true interrater reliabilities. BMC Medical Research Methodology, 22. https://doi.org/10.1186/s12874-022-01707-5

Zickar, M. (2020). Measurement Development and Evaluation. Annual Review of Organizational Psychology and Organizational Behavior. https://doi.org/10.1146/annurev-orgpsych-012119-044957