Research Article

Pre-service science teachers’ perception on using generative artificial intelligence in science education

Izida I. Ishmuradova 1 * , Sergei P. Zhdanov 2 , Sergey V. Kondrashev 3 , Natalya S. Erokhova 4 , Elena E. Grishnova 5 , Nonna Yu. Volosova 6
More Detail
1 Kazan (Volga region) Federal University, Naberezhnye Chelny Institute, Naberezhnye Chelny, RUSSIA2 Department of Philosophy, Political Science, Sociology named after G.S. Arefieva, National Research University «Moscow Power Engineering Institute», Moscow, RUSSIA3 I. M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, RUSSIA4 Peoples’ Friendship University of Russia (RUDN University), Moscow, RUSSIA5 Bauman Moscow State Technical University, Moscow, RUSSIA6 Orenburg State University, Orenburg, RUSSIA* Corresponding Author
Contemporary Educational Technology, 17(3), July 2025, ep579, https://doi.org/10.30935/cedtech/16207
Published Online: 27 March 2025, Published: 01 July 2025
OPEN ACCESS   5342 Views   6063 Downloads
Download Full Text (PDF)

ABSTRACT

The development of generative artificial intelligence (AI) has started a conversation on its possible uses and inherent difficulties in the field of education. It becomes essential to understand the perceptions of pre-service teachers about the integration of this technology into teaching practices as AI models including ChatGPT, Claude, and Gemini acquire popularity. This investigation sought to create a valid and trustworthy instrument for evaluating pre-service science teachers’ opinions on the implementation of generative AI in educational settings related to science. This work was undertaken within the faculty of education at Kazan Federal University. The total number of participants is 401 undergraduate students. The process of scale development encompassed expert evaluation for content validity, exploratory factor analysis, confirmatory factor analysis, and assessments of reliability. The resultant scale consisted of four dimensions: optimism and utility of AI in science education, readiness and openness to AI integration, AI’s role in inclusivity and engagement, and concerns and skepticism about AI in science education. The scale demonstrated robust psychometric properties, evidenced by elevated reliability coefficients. Cluster analysis unveiled distinct profiles of pre-service teachers based on their responses, encompassing a spectrum from enthusiastic participants to skeptical disengaged individuals. This study provides a comprehensive instrument for evaluating pre-service teachers’ perceptions, thereby informing teacher education programs and professional development initiatives regarding the responsible integration of AI. Recommendations entail the validation of the scale across varied contexts, the exploration of longitudinal changes, and the investigation of subject-specific applications of generative AI in science education.
Keywords: generative artificial intelligence, science education, scale development, pre-service teacher’s perceptions

CITATION (APA)

Ishmuradova, I. I., Zhdanov, S. P., Kondrashev, S. V., Erokhova, N. S., Grishnova, E. E., & Volosova, N. Y. (2025). Pre-service science teachers’ perception on using generative artificial intelligence in science education. Contemporary Educational Technology, 17(3), ep579. https://doi.org/10.30935/cedtech/16207

REFERENCES

  1. Alasadi, E. A., & Baiz, C. R. (2023). Generative AI in education and research: Opportunities, concerns, and solutions. Journal of Chemical Education, 100(8), 2965–2971. https://doi.org/10.1021/acs.jchemed.3c00323
  2. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
  3. Bakkum, M. J., Hartjes, M. G., Piët, J. D., Donker, E. M., Likic, R., Sanz, E., de Ponti, F., Verdonk, P., Richir, M. C., van Agtmael, M. A., & Tichelaar, J. (2023). Using artificial intelligence to create diverse and inclusive medical case vignettes for education. British Journal of Clinical Pharmacology, 90(3), 640–648. https://doi.org/10.1111/bcp.15977
  4. Baytak, A. (2023). The acceptance and diffusion of generative artificial intelligence in education: A literature review. Current Perspectives in Educational Research, 6(1), 7–18. https://doi.org/10.46303/cuper.2023.2
  5. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? FAccT’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
  6. Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, Article 100068. https://doi.org/10.1016/j.caeai.2022.100068
  7. Brown, T. A. (2015). Confirmatory factor analysis for applied research. The Guilford Press.
  8. Cevallos, A., Latorre, L., Alicandro, G., Wanner, Z., Cerrato, I., Zarate, J. D., Alvarez, J., Villacreses, K., Pfeifer, M., Gutierrez, M., Villanueva, V., Rivera-Fournier, A., Riobó, A., Pombo, C., Puerto, F., & Rodriguez Breuning, J. (2023). Generative AI. TechREPORT: https://doi.org/10.18235/0005105
  9. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8
  10. Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182. https://doi.org/10.1007/s10796-022-10291-4
  11. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y
  12. Cun, A., & Huang, T. (2024). Generative AI and TPACK in teacher education: Pre-service teachers’ perspectives. In M. Searson, E. Langran, & J. Trumble (Eds.), Exploring new horizons: Generative artificial intelligence and teacher education (pp. 62–75). AACE.
  13. Hwang, K., Challagundla, S., Alomair, M. M., Karen Chen, L., & Choa, F.-S. (2023). Towards AI-assisted multiple choice question generation and quality evaluation at scale: Aligning with Bloom’s taxonomy. In Proceedings of the NeurIPS’23 Workshop on Generative AI for Education (pp. 1–8).
  14. Istenic, A., Bratko, I., & Rosanda, V. (2021). Are pre-service teachers disinclined to utilise embodied humanoid social robots in the classroom? British Journal of Educational Technology, 52(6), 2340–2358. https://doi.org/10.1111/bjet.13144
  15. Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23. https://doi.org/10.1037/a0014694
  16. Joseph, J. (2023). Assessing the potential of laboratory instructional tool through Synthesia AI: A case study on student learning outcome. Journal of E-Learning and Higher Education, 18(2), 5–16.
  17. Kaldaras, L., Akaeze, H. O., & Reckase, M. D. (2024). Developing valid assessments in the era of generative artificial intelligence. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1399377
  18. Karampelas, K. (2025). Integration of artificial intelligence in national science curricula. Contemporary Mathematics and Science Education, 6(1), Article ep25002. https://doi.org/10.30935/conmaths/15883
  19. Karaoglan Yilmaz, F. G., & Yilmaz, R. (2020). Student opinions about personalized recommendation and feedback based on learning analytics. Technology, Knowledge and Learning, 25, 753–768. https://doi.org/10.1007/s10758-020-09460-8
  20. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence, 5, Article 100156. https://doi.org/10.1016/j.caeai.2023.100156
  21. Krishna Suryadevara, C. (2020). Generating free images with OpenAI’s generative models. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56.
  22. Kumar, D., Haque, A., Mishra, K., Islam, F., Kumar Mishra, B., & Ahmad, S. (2023). Exploring the transformative role of artificial intelligence and metaverse in education: A comprehensive review. Metaverse Basic and Applied Research, 2, Article 55. https://doi.org/10.56294/mr202355
  23. Langran, E., Searson, M., & Trumble, J. (2024). Transforming teacher education in the age of generative AI. In M. Searson, E. Langran, & J. Trumble (Eds.), Exploring new horizons: Generative artificial intelligence and teacher education (pp. 2–13). AACE.
  24. Lee, S., & Song, K. (2024). Teachers’ and students’ perceptions of AI-generated concept explanations: Implications for integrating generative AI in computer science education. Computers and Education: Artificial Intelligence, 7, Article 100283. https://doi.org/10.1016/j.caeai.2024.100283
  25. Leiker, D., Gyllen, A. R., Eldesouky, I., & Cukurova, M. (2023). Generative AI for learning: Investigating the potential of learning videos with synthetic virtual instructors. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners, doctoral consortium and blue sky. AIED 2023. Communications in computer and information science, vol 1831 (pp. 523–529). Springer. https://doi.org/10.1007/978-3-031-36336-8_81
  26. Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. The Lancet Digital Health, 5(3), E105–E106). https://doi.org/10.1016/S2589-7500(23)00019-5
  27. 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), Article 856. https://doi.org/10.3390/educsci13090856
  28. Mohamed, A. M. (2023). Exploring the potential of an AI-based chatbot (ChatGPT) in enhancing English as a foreign language (EFL) teaching: Perceptions of EFL faculty members. Education and Information Technologies, 29, 3195–3217. https://doi.org/10.1007/s10639-023-11917-z
  29. Nyaaba, M., & Zhai, X. (2024). Generative AI professional development needs for teacher educators. Journal of AI, 8(1), 1–13. https://doi.org/10.61969/jai.1385915
  30. Nyaaba, M., Kyeremeh, P., Majialuwe, E. K., Owusu-fordjour, C., Asebiga, E., & A-ingkonge, B. (2024). Generative AI in academic research: A descriptive study on awareness, gender usage, and views among pre-service teachers. Journal of AI, 8(1), 45–60. https://doi.org/10.61969/jai.1400867
  31. Okunade, A. I. (2024). The role of artificial intelligence in teaching of science education in secondary schools in Nigeria. European Journal of Computer Science and Information Technology, 12(1), 57–67. https://doi.org/10.37745/ejcsit2013/vol12n15767
  32. Poola, I., & Božić, V. (2023). Guiding AI with human intuition for solving mathematical problems in Chat GPT. International Journal of Engineering & Scientific Research, 11(7), 31–40.
  33. Rasul, T., Nair, S., Kalendra, D., Robin, M., Santini, F. de O., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41–56. https://doi.org/10.37074/jalt.2023.6.1.29
  34. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342–363. https://doi.org/10.37074/jalt.2023.6.1.9
  35. Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), Article 11524. https://doi.org/10.3390/su151511524
  36. Shumiye, Y. D. (2024). Artificial intelligence program implementation in STEM education: Cases of Ethiopia STEM center. European Journal of Health and Biology Education, 11(1), 7–12.
  37. Söğüt, S. (2024). Generative artificial intelligence in EFL writing: A pedagogical stance of pre-service teachers and teacher trainers. Focus on ELT Journal Special Issue, 6(1), 58–73. https://doi.org/10.14744/felt.6.1.5
  38. Spaniol, M. J., & Rowland, N. J. (2023). AI-assisted scenario generation for strategic planning. Futures and Foresight Science, 5(2), Article e148. https://doi.org/10.1002/ffo2.148
  39. Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423
  40. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association. https://doi.org/10.1037/10694-000
  41. Treglia, E., & Tomassoni, R. (2024). Creativity and generative AI in educational contexts: Challenges and future scenarios. Survey on the perceptions of students and teachers. Italian Journal of Health Education, Sports and Inclusive Didactics, 8(2). https://doi.org/10.32043/gsd.v8i2.1147
  42. Vallis, C., Wilson, S., Gozman, D., & Buchanan, J. (2023). Student perceptions of AI-generated avatars in teaching business ethics: We might not be impressed. Postdigital Science and Education, 6, 537–555. https://doi.org/10.1007/s42438-023-00407-7
  43. Verma, G., Campbell, T., Melville, W., & Park, B. Y. (2023). Navigating opportunities and challenges of artificial intelligence: ChatGPT and generative models in science teacher education. Journal of Science Teacher Education, 34(8), 793–798. https://doi.org/10.1080/1046560X.2023.2263251
  44. Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
  45. Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Journal of Emergency Primary Health Care, 8(3), 1–13. https://doi.org/10.33151/ajp.8.3.93
  46. Wilton, L., Ip, S., Sharma, M., & Fan, F. (2022). Where is the AI? AI literacy for educators. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners’ and doctoral consortium. AIED 2022. Lecture notes in computer science, vol 13356 (pp. 180–188). Springer. https://doi.org/10.1007/978-3-031-11647-6_31
  47. Wood, D., & Moss, S. H. (2024). Evaluating the impact of students’ generative AI use in educational contexts. Journal of Research in Innovative Teaching & Learning, 17(2), 152–167. https://doi.org/10.1108/JRIT-06-2024-0151
  48. Yang, T. C., & Chen, J. H. (2023). Pre-service teachers’ perceptions and intentions regarding the use of chatbots through statistical and lag sequential analysis. Computers and Education: Artificial Intelligence, 4, Article 100119. https://doi.org/10.1016/j.caeai.2022.100119
  49. Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79–94. https://doi.org/10.20982/tqmp.09.2.p079
  50. Yusoff, M. S. B. (2019). ABC of content validation and content validity index calculation. Education in Medicine Journal, 11(2), 49–54. https://doi.org/10.21315/eimj2019.11.2.6
  51. Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S. (2023). Generative AI in computing education: Perspectives of students and instructors. In Proceedings of the 2023 IEEE Frontiers in Education Conference (pp. 1–9). IEEE. https://doi.org/10.1109/FIE58773.2023.10343467
  52. Zhang, C., & Villanueva, L. E. (2023). Generative artificial intelligence preparedness and technological competence. International Journal of Education and Humanities, 11(2), 164–170. https://doi.org/10.54097/ijeh.v11i2.13753
  53. 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, Article 49. https://doi.org/10.1186/s41239-023-00420-7
  54. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. In Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025