The role of academic self-efficacy in pre-service mathematics and science teachers' use of generative artificial intelligence tools

Abstract

Generative Artificial Intelligence (GenAI) has emerged as a transformative technology in recent years, fundamentally reshaping traditional pedagogical approaches and educational environments. The objective of this study was to investigate the adoption of AI tools by pre-service mathematics and science teachers in their learning processes, as well as to assess the influence of academic self-efficacy (ASE) on this adoption, framed through the Technology Acceptance Model (TAM). Specifically, this research evaluated the effect of ASE on the acceptance of AI technologies by pre-service mathematics and science teachers and their intentions to utilize these technologies in the future. Data were collected from a sample of 146 pre-service mathematics and 91 pre-service science teachers (N=237) at an educational faculty in a university located in Western Türkiye during the spring semester of 2024. The data collection employed two distinct instruments: the first instrument comprised items from the Academic Self-Efficacy Scale to assess levels of academic self-efficacy, while the second instrument included items adapted from the TAM, TAM2 (Technology Acceptance Model), and UTAUT (Unified Theory of Acceptance and Use of Technology) frameworks. The results of hypothesis testing indicated that pre-service teachers with elevated levels of ASE had a more favorable perception of the usefulness and ease of use of GenAI tools, which in turn positively influenced their intention to adopt AI-based technologies. Furthermore, the study revealed that perceived usefulness and ease of use significantly shaped pre-service teachers' attitudes and behavioral intentions toward AI. When pre-service teachers recognize GenAI as a beneficial learning resource and find it user-friendly, their willingness to engage with it increases. This study posits that ASE is a critical factor in the acceptance of GenAI-based tools among pre-service mathematics and science teachers, thereby affirming the TAM as a relevant and effective model for examining pre-service teachers' potential engagement with AI in educational contexts.

Keywords

• Pre-service mathematics teachers
• Pre-service science teachers
• Academic self-efficacy
• Technology acceptance model

Matematik ve fen bilgisi öğretmen adaylarının üretken yapay zeka araçlarını kullanmalarında akademik öz yeterliğin rolü

Abstract

Üretken Yapay Zeka (ÜYZ), son yıllarda geleneksel pedagojik yaklaşımları ve eğitim ortamlarını temelden yeniden şekillendiren dönüştürücü bir teknoloji olarak ortaya çıkmıştır. Bu çalışmanın amacı, matematik ve fen bilgisi öğretmen adaylarının öğrenme süreçlerinde yapay zeka araçlarını benimsemelerini araştırmak ve Teknoloji Kabul Modeli (TAM) çerçevesinde akademik öz yeterliğin (AÖY) bu benimseme üzerindeki etkisini değerlendirmektir. Bu araştırma AÖY'in matematik ve fen bilgisi öğretmen adayları tarafından yapay zeka teknolojilerinin kabulü ve gelecekte bu teknolojileri kullanma niyetleri üzerindeki etkisini değerlendirmiştir. Veriler, 2024 yılı bahar döneminde Türkiye'nin batısında bir üniversitenin eğitim fakültesinde 146 matematik ve 91 fen bilgisi öğretmen adayından oluşan bir örneklemden toplanmıştır (N=237). Veri toplamada iki farklı ölçek kullanılmıştır: İlk ölçek akademik öz yeterlik düzeylerini değerlendirmek için Akademik Öz Yeterlik Ölçeği, ikinci ölçek TAM, TAM2 (Teknoloji Kabul Modeli 2) ve UTAUT (Teknoloji Kabul ve Kullanım Birleştirilmiş Modeli) çerçevelerinden uyarlanmış maddeler içermektedir. Hipotez testinin sonuçları, yüksek düzeyde AÖY'e sahip öğretmen adaylarının ÜYZ araçlarının kullanışlılığı ve kullanım kolaylığı konusunda daha olumlu bir algıya sahip olduklarını ve bunun da YZ tabanlı teknolojileri benimseme niyetlerini olumlu yönde etkilediğini göstermiştir. Ayrıca, çalışma, algılanan yararlılık ve kullanım kolaylığının öğretmen adaylarının ÜYZ'ye yönelik tutumlarını ve davranışsal niyetlerini önemli ölçüde şekillendirdiğini ortaya koymuştur. Öğretmen adayları ÜYZ'yi faydalı bir öğrenme kaynağı olarak gördüklerinde ve kullanıcı dostu bulduklarında, onunla etkileşim kurma istekleri artmaktadır. Bu çalışma, ASE'nin matematik ve fen bilgisi öğretmen adayları arasında ÜYZ tabanlı araçların kabulünde kritik bir faktör olduğunu ve böylece TAM'ın öğretmen adaylarının eğitim bağlamlarında ÜYZ ile potansiyel etkileşimlerini incelemek için uygun ve etkili bir model olduğunu doğrulamaktadır.

Keywords

• Matematik öğretmen adayları
• Fen bilgisi öğretmen adayları
• Akademik öz-yeterlik
• Teknoloji kabul modeli

References

1. Radhwan, M. G., Opportunities, Challenges, and Strategies for Integrating Generative Artificial Intelligence in Teaching and Learning in Higher Education. In Prompt Engineering and Generative AI Applications for Teaching and Learning, edited by Areej ElSayary, 103–124, IGI Global, (2025).
2. Wu, X. Y., & Chiu, T. K. F., Integrating learner characteristics and generative AI affordances to enhance self-regulated learning: a configurational analysis. Journal of New Approaches in Educational Research, 14, 10 (2025).
3. Abdulayeva, A., Zhanatbekova, N., Andasbayev, Y., & Boribekova, F., Fostering AI literacy in pre-service physics teachers: inputs from training and co-variables. Frontiers in Education, 10, 1505420, (2025).
4. Zhang, C., Schießl, J., Plößl, L., et al., Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20, 49, (2023).
5. Song, L., Improving pre-service teachers' self-efficacy on technology integration through service learning. The Canadian Journal of Action Research, 19, 1, 22–32, (2018).
6. Lemon, N., & Garvis, S., Pre-service teacher self-efficacy in digital technology. Teachers and Teaching, 22, 3, 387–408, (2015).
7. Zee, M., & Koomen, H. M., Teacher Self-Efficacy and Its Effects on Classroom Processes, Student Academic Adjustment, and Teacher Well-Being: A Synthesis of 40 Years of Research. Review of Educational Research, 86, 981–1015, (2016).
8. Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 3, 319–340, (1989).

9. Venkatesh, V., & Davis, F. D., A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 2, 186–204, (2000).
10. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D., User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 3, 425–478, (2003).
11. Teo, T., Modeling the determinants of pre-service teachers’ perceived usefulness of e-learning. Campus-Wide Information Systems, 28, 2, 124–140, (2011).
12. Bandura, A., Self-efficacy: The exercise of control. W.H. Freeman, (1997).
13. Schunk, D. H., & Pajares, F., Self-Efficacy Theory. In Handbook of Motivation at School. Routledge, (2009).
14. Tapan Broutin, M. S., Exploring Mathematics Teacher Candidates’ Instrumentation Process of Generative Artificial Intelligence for Developing Lesson Plans. Yükseköğretim Dergisi, 14, 1, 165–176, (2024).
15. Liu, M., Ren, Y., Nyagoga, L. M., Stonier, F., Wu, Z., & Yu, L., Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Educational Research, 1, 1, 72–101, (2023).
16. Tapalova, O., & Zhiyenbayeva, N., Artificial intelligence in education: AIEd for personalised learning pathways. Electronic Journal of e-Learning, 20, 5, 639–653, (2022).
17. Lee, G., & Zhai, X., Using ChatGPT for science learning: A study on pre-service teachers' lesson planning. IEEE Transactions on Learning Technologies, 17, 1683–1700, (2024).
18. AbuSahyon, A. S. A. E., Alzyoud, A., Alshorman, O., & Al-Absi, B., AI-driven technology and chatbots as tools for enhancing English language learning in the context of second language acquisition: A review study. International Journal, 10, 1, 1209–1223, (2023).
19. Kolluru, V., Mungara, S., & Chintakunta, A. N., Adaptive learning systems: Harnessing AI for customized educational experiences. International Journal of Computational Science and Information Technology (IJCSITY), 6, 1, 2, (2018).
20. Mishra, P., Koehler, M. J., & Henriksen, D., The Seven Trans-Disciplinary Habits of Mind: Extending the TPACK Framework Towards 21st Century Learning. Educational Technology, 51, 2, 22–28, (2011).
21. Chen, X., Xie, H., Zou, D., & Hwang, G. J., Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002, (2020).
22. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B., Intelligence unleashed: An argument for AI in Education. Pearson, (2016).
23. Roschelle, J., Lester, J., & Fusco, J., AI and the future of learning: Expert panel report. Digital Promise, (2020).
24. Andersen, C. L., & West, R. E., Improving mentoring in higher education in undergraduate education and exploring implications for online learning. Revista de Educación a Distancia (RED), 20, 64, (2020).
25. Tate, T., & Warschauer, M., Access, digital writing, and achievement: The data in two diverse school districts. Journal of Writing Assessment, 15, 1, 1–36, (2022).
26. Bates, A. W., Teaching in a Digital Age – Second Edition. Tony Bates Associates Ltd., (2019).
27. Pajares, F., Self-efficacy beliefs in academic settings. Review of Educational Research, 66, 4, 543–578, (1996).
28. Schunk, D. H., & DiBenedetto, M. K., Academic self-efficacy. In Handbook of Positive Psychology in Schools. Routledge, (2022).
29. Zimmerman, B. J., Bandura, A., & Martinez-Pons, M., Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29, 3, 663–676, (1992).
30. Bingölbali, A., & Karakaya, Y. E., Investigation of academic self-efficacy according to perceptions of sports high school students. Fırat Üniversitesi Sosyal Bilimler Dergisi, 31, 2, 607–613, (2021).
31. Høigaard, R., Kovač, V. B., Øverby, N. C., & Haugen, T., Academic self-efficacy mediates the effects of school psychological climate on academic achievement. School Psychology Quarterly, 30, 1, 64, (2015).
32. Ferla, J., Valcke, M., & Cai, Y., Academic self-efficacy and academic self-concept: Reconsidering structural relationships. Learning and Individual Differences, 19, 4, 499–505, (2009).
33. Lent, R. W., Brown, S. D., & Gore Jr, P. A., Discriminant and predictive validity of academic self-concept, academic self-efficacy, and mathematics-specific self-efficacy. Journal of Counselling Psychology, 44, 3, 307, (1997).
34. Yurt, E., The relationships among pre-service teachers’ critical thinking disposition, self-efficacy, and creative thinking disposition in Turkey: a latent growth mediation model. Current Psychology, 44, 85–102, (2025).
35. Burak, S., Self-efficacy of pre-school and primary school pre-service teachers in musical ability and music teaching. International Journal of Music Education, 37, 2, 257–271, (2019).
36. Sultan, A. A., Henson, H., & Fadde, P. J., Pre-service elementary teachers’ scientific literacy and self-efficacy in teaching science. IAFOR Journal of Education, 6, 1, (2018).
37. Bleicher, R. E., Nurturing Confidence in Preservice Elementary Science Teachers. Journal of Science Teacher Education, 18, 6, 841–860, (2007).
38. Velthuis, C., Fisser, P., & Pieters, J. M., Teacher training and pre-service primary teachers’ self-efficacy for science teaching. Journal of Science Teacher Education, 25, 4, 445–464, (2014).
39. Slater, E., & Main, S., A measure of classroom management: validation of a pre-service teacher self-efficacy scale. Journal of Education for Teaching, 46, 5, 616–630, (2020).
40. Giles, R. M., & Kent, A. M., An investigation of pre-service teachers’ self-efficacy for teaching with technology. Asian Education Studies, 1, 1, 32, (2016).
41. Holden, H. K., & Rada, R., Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43, 4, 343–367, (2011).
42. Pendergast, D., Garvis, S., & Keogh, J., Pre-service student-teacher self-efficacy beliefs: an insight into the making of teachers. Australian Journal of Teacher Education, 36, 12, (2011).
43. Caner, M., & Aydın, S., Self efficacy beliefs of pre-service teachers on technology integration. Turkish Online Journal of Distance Education, 79–94, (2021).
44. Keser, H., Yılmaz, F. G. K., & Yılmaz, R., TPACK competencies and technology integration self-efficacy perceptions of pre-service teachers. İlköğretim Online, 14, 2, (2015).
45. Karasar, N., Bilimsel araştırma modelleri (25. Baskı). Nobel Yayıncılık, (2013).
46. Liaw, S. S., & Huang, H. M., Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60, 1, 14–24, (2013).
47. Chemers, M. M., Hu, L., & Garcia, B. F., Academic self-efficacy and first-year college student performance and adjustment. Journal of Educational Psychology, 93, 1, 55–64, (2001).
48. Hanham, J., Lee, C. B., & Teo, T., The influence of technology acceptance, academic self-efficacy, and gender on academic achievement through online tutoring. Computers & Education, 172, 104252, (2021).
49. Zimmerman, B. J., & Schunk, D. H., Self-regulated learning and academic achievement: Theoretical perspectives. Routledge, (2011).
50. Ajzen, I., The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 2, 179–211, (1991).
51. Venkatesh, V., & Bala, H., Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39, 2, 273–315, (2008).
52. Park, S. Y., Nam, M. W., & Cha, S. B., University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43, 4, 592–605, (2012).
53. Kandemir, M., An explanatory model of academic procrastination behaviour. Doctoral dissertation, Gazi University, (2010).
54. Venkatesh, V., & Davis, F. D., A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 3, 451–481, (1996).
55. Schumacher, R. E., & Lomax, R. G., A beginner's guide to structural equation modeling (3rd ed.), Routledge Pub., (2010).
56. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P., Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 5, 879–903, (2003).
57. Ullman, J. B., & Bentler, P. M., Structural equation modeling. In Handbook of Psychology, Second Edition. Eds I. Weiner, J. A. Schinka and W. F. Velicer, John Wiley & Sons, Inc., (2012).
58. Field, A., Discovering statistics using IBM SPSS statistics. Sage, (2024).
59. Kock, F., Berbekova, A., & Assaf, A. G., Understanding and managing the threat of common method bias: Detection, prevention and control. Tourism Management, 86, 104330, (2021).
60. Byrne, B. M., Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge, (2013).
61. Von Eye, A., & Bogat, G. A., Testing the assumption of multivariate normality. Psychology Science, 46, 243–258, (2004).
62. Tabachnick, B. G., Fidell, L. S., & Ullman, J. B., Using multivariate statistics. Pearson, (2013).
63. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C., Multivariate data analysis (4th ed.): With readings. Prentice-Hall, Inc., (1995).
64. George, D., & Mallery, P., IBM SPSS Statistics 25 Step by Step. Routledge, (2019).
65. Taber, K. S., The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 6, 1273–1296, (2018).
66. Bagozzi, R. P., & Phillips, L. W., Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 27, 3, 459–489, (1982).
67. Hu, L., & Bentler, P. M., Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological Methods, 3, 4, 424–453, (1998).
68. Bandura, A., & Zimmerman, B. J., Self-regulation of academic studying: The role of self-efficacy and self-regulatory skill. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 1–19). Lawrence Erlbaum Associates, (1994).
69. Pan, X., Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: learning motivation as a mediator. Frontiers in Psychology, 11, 1–11, (2020).
70. Yulian, R., Yuniarti, Y., & Lee, N. A. A., Technology acceptance of augmented reality to attitude and self-efficacy in learning English. Journal of English Language Teaching Innovations and Materials (Jeltim), 4, 2, 127, (2022).
71. Budu, K. W. A., Mu, Y., & Mireku, K. K., Investigating the effect of behavioral intention on e-learning systems usage: empirical study on tertiary education institutions in Ghana. Mediterranean Journal of Social Sciences, 9, 3, 201–216, (2018).
72. Al Kurdi, B., Alshurideh, M., & Salloum, S. A., Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering (IJECE), 10, 6, 6484–6496, (2020).
73. Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A., Extending the technology acceptance model (TAM) to predict university students' intentions to use metaverse-based learning platforms. Education and Information Technologies, 28, 11, 15381–15413, (2023).
74. Latip, M. S. A., Tamrin, M., Noh, I., Rahim, F. A., Nur, S., & Latip, N. A., Factors affecting e-learning acceptance among students: The moderating effect of self-efficacy. International Journal of Information and Education Technology, 12, 2, 116–122, (2022).
75. Fishbein, M., & Ajzen, I., Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley, (1975).
76. Yanto, D. T. P., Ganefri, Hastuti, Candra, O., Kabatiah, M., Andrian, … & Zaswita, H., The affecting factors of students' attitudes toward the use of a virtual laboratory: a study in industrial electrical engineering. International Journal of Online and Biomedical Engineering (IJOE), 19, 13, 4–16, (2023).
77. Jang, J., Ko, Y., Shin, W. S., & Han, I., Augmented reality and virtual reality for learning: an examination using an extended technology acceptance model. IEEE Access, 9, 6798–6809, (2021).
78. Venkatesh, V., Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 4, 342–365, (2000).
79. Vinnikova, A., Lu, L., Wei, J., Fang, G., & Yan, J., The use of smartphone fitness applications: the role of self-efficacy and self-regulation. International Journal of Environmental Research and Public Health, 17, 20, 7639, (2020).
80. Kong, S. C., Yang, Y., & Hou, C., Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, 100328, (2024).
81. Eldakar, M. A., Shehata, A. M., & Ammar, A. S., What motivates academics in Egypt toward generative AI tools? An integrated model of TAM, SCT, UTAUT2, perceived ethics, and academic integrity. Information Development, 02666669251314859, (2025).
82. Tantivejakul, N., Chantharasombat, J., & Kongpolphrom, W., Voices of the future: exploring students' views on the use of GenAI in academic and professional pr writing. LEARN Journal: Language Education and Acquisition Research Network, 17, 2, 511–537, (2024).
83. Hsiao, C. H., & Tang, K. Y., Beyond acceptance: an empirical investigation of technological, ethical, social, and individual determinants of GenAI-supported learning in higher education. Education and Information Technologies, 1–26, (2024).
84. Shata, A., & Hartley, K., Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI. International Journal of Educational Technology in Higher Education, 22, 1, 14, (2025).
85. Tan, P., Factors Affecting Students' Continuance Usage Intention of Generative Artificial Intelligence (GenAI) in Higher Education. Doctoral dissertation, Tunku Abdul Rahman University of Management and Technology, (2024).
86. Collie, R. J., Martin, A. J., & Gasevic, D., Teachers’ generative AI self-efficacy, valuing, and integration at work: Examining job resources and demands. Computers and Education: Artificial Intelligence, 7, 100333, (2024).