Open Access Peer-reviewed Review

Generative AI in Pre-Service Science Teacher Education: A Systematic Review

Article Sidebar

PRISMA review processes
Download PDF
Submitted   Apr 15, 2025
Published   Jul 30, 2025
Issue    Vol 5 No 2 (2025)
DOI   10.25082/AMLER.2025.02.007

Views

0

Downloads

0

Citations

PlumX Metrics

Main Article Content

Theodoros Spasopoulos corresponding author
Department of Special Education, University of Thessaly, Volos, Greece
Dimitris Sotiropoulos
Department of Special Education, University of Thessaly, Volos, Greece
Michail Kalogiannakis
Department of Special Education, University of Thessaly, Volos, Greece

Abstract

Despite the increasing adoption of Generative Artificial Intelligence (GenAI) in education, there is a lack of comprehensive reviews on how GenAI is being utilized within Pre-Service Teachers (PSTs) in science. This systematic literature review (SLR) aims to address this gap by examining the extent and nature of GenAI integration in future science teachers' preparation programs. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 21 peer-reviewed empirical studies published between 2022 and 2025 were identified and analyzed through qualitative thematic synthesis. The analysis addresses three research questions: 1) the extent to which GenAI is used in the curriculum of PSTs in science education; 2) how PSTs use GenAI tools to develop a deeper understanding of science and develop scientific reasoning; and 3) how PSTs in science education are using GenAI tools to plan and carry out teaching activities. Findings reveal that the integration of GenAI into curricula remains fragmented and often experimental, typically confined to technology-related courses or pilot projects. PSTs primarily utilize GenAI tools for conceptual clarification, hypothesis generation, and self-regulated learning. Furthermore, these tools serve as cognitive partners in designing lesson plans, differentiating instruction, and simulating classroom scenarios. However, the absence of structured pedagogical guidance often leads to superficial use and limited critical evaluation of AI-generated content. This review highlights the transformative potential of GenAI in science education while underscoring the need for institutional frameworks, faculty training, and the development of AI literacy. Future research should focus on how to sustainably integrate GenAI into teacher education to foster scientific reasoning, pedagogical adaptability, and responsible use of technology.

Keywords
Generative AI, ChatGPT, Large Language Models, Pre-Service Teacher Education, PSTs, PRISMA

Article Details

How to Cite
Spasopoulos, T., Sotiropoulos, D., & Kalogiannakis, M. (2025). Generative AI in Pre-Service Science Teacher Education: A Systematic Review. Advances in Mobile Learning Educational Research, 5(2), 1501-1523. https://doi.org/10.25082/AMLER.2025.02.007
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Al Abri, M., Al Mamari, A., & Al Marzouqi, Z. (2025). Exploring the implications of generative-AI tools in teaching and learning practices. Journal of Education and E-Learning Research, 12(1), 31–41. https://doi.org/10.20448/jeelr.v12i1.6355
  2. A. Hayudini, M. A., K. Tahil, S., M. Marsin, J., A. Kiram, D. A.-M., A. Amirul, A., Alegado Jimpoy, A. J., B. Sahibad, F., P. Hamsaji, A., H. Abduljalil, A., & V. Inoferio, H. (2025). Learning activities in STEM to sustain curiosity of Gen-Z in an AI-dependent college setting. Environment and Social Psychology, 10(4), e3215. https://doi.org/10.59429/esp.v10i4.3215
  3. Abualrob, M. M. (2025). Innovative teaching: How pre-service teachers use artificial intelligence to teach science to fourth graders. Contemporary Educational Technology, 17(1), ep547. https://doi.org/10.30935/cedtech/15686
  4. Amar, S., & Benchouk, K. (2024). The Role of Generative AI in Higher Education: Fostering Teamwork and Interaction as a Virtual Teaching Assistance. General Aspects of Applying Generative AI in Higher Education, 39–53. https://doi.org/10.1007/978-3-031-65691-0_3
  5. Ampartzaki, M., Tassis, K., Kalogiannakis, M., Pavlidou, V., Christidis, K., Chatzoglidou, S., & Eleftherakis, G. (2024). Assessing the Initial Outcomes of a Blended Learning Course for Teachers Facilitating Astronomy Activities for Young Children. Education Sciences, 14(6), 606. https://doi.org/10.3390/educsci14060606
  6. Arantes, J. (2024). Understanding Intersections Between GenAI and Pre-Service Teacher Education: What Do We Need to Understand About the Changing Face of Truth in Science Education? Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10189-7
  7. Aslanyan-rad, E. (2024). A Presentation of An Extended lens of Technology of ChatGPT-based TPACK Theory dimensions: An Exploratory Systematic Literature Review Scope. https://doi.org/10.13140/RG.2.2.31533.29929
  8. Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, 100179. https://doi.org/10.1016/j.caeo.2024.100179
  9. Bae, H., Hur, J., Park, J., Choi, G. W., & Moon, J. (2024). Pre-service Teachers’ Dual Perspectives on Generative AI: Benefits, Challenges, and Integrating into Teaching and Learning. Online Learning, 28(3), 131–156. https://doi.org/10.24059/olj.v28i3.4543
  10. Bahtaji, M. A. A. (2021). The Role of Math and Science Exposure on the Effect of 5e Instructional Model in Physics Conceptions. Journal of Baltic Science Education, 20(1), 10–20. Internet Archive. https://doi.org/10.33225/jbse/21.20.10
  11. Barbieri, W., & Nguyen, N. (2025). Generative AI as a “placement buddy”: Supporting pre-service teachers in work-integrated learning, self-management and crisis resolution. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.10035
  12. Blonder, R., Feldman-Maggor, Y., & Rap, S. (2024). Are They Ready to Teach? Generative AI as a Means to Uncover Pre-Service Science Teachers’ PCK and Enhance Their Preparation Program. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10180-2
  13. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  14. Eager, B., & Brunton, R. (2023). Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice. Journal of University Teaching and Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.02
  15. Buck, G. A., Trauth‐Nare, A., & Kaftan, J. (2009). Making formative assessment discernable to pre‐service teachers of science. Journal of Research in Science Teaching, 47(4), 402–421. Portico. https://doi.org/10.1002/tea.20344
  16. Carracedo, J. M. C. (2025). Inquiry-Based Learning in Phonetics and Phonology: Promotion of Critical Thinking Skills in an EFL Higher Education Context. International Journal of Instruction, 18(1), 1–22. https://doi.org/10.29333/iji.2025.1811a
  17. Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
  18. Ceylan, B., & Altiparmak Karakus, M. (2024). Development of an Artificial Intelligence-Based Mobile Application Platform: Evaluation of Prospective Science Teachers’ Project on Creating Virtual Plant Collections in terms of Plant Blindness and Knowledge. International Journal of Technology in Education and Science, 8(4), 668–688. https://doi.org/10.46328/ijtes.595
  19. Chai, C. S., Koh, J. H. L., & Tsai, C. C. (2013). A review of technological pedagogical content knowledge. Journal of Educational Technology & Society, 16(2), 31-51. https://www.jstor.org/stable/jeductechsoci.16.2.31
  20. Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187–6203. https://doi.org/10.1080/10494820.2023.2253861
  21. Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197
  22. 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
  23. Cooper, G., Tang, K.-S., & Fitzgerald, A. (2025). Intersections of Mind and Machine: Navigating the Nexus of Artificial Intelligence, Science Education, and the Preparation of Pre-service Teachers. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-025-10200-9
  24. Doorsamy, W., Padayachee, K., & Cornell, A. S. (2025). Generative Artificial Intelligence and Encounters with Knowledge in STEM Higher Education Curricula. 2025 IEEE Global Engineering Education Conference (EDUCON), 1–5. https://doi.org/10.1109/educon62633.2025.11016322
  25. Dyachenko, A., Mukanova, R., & Erkibayeva, M. (2024). Integration of interdisciplinary connections between chemistry, physics, and biology in the education of secondary school students. International Journal of Educational Reform. https://doi.org/10.1177/10567879241290180
  26. Feldman-Maggor, Y., Blonder, R., & Alexandron, G. (2024). Perspectives of Generative AI in Chemistry Education Within the TPACK Framework. Journal of Science Education and Technology, 34(1), 1–12. https://doi.org/10.1007/s10956-024-10147-3
  27. van den Berg, G., & du Plessis, E. (2023). ChatGPT and Generative AI: Possibilities for Its Contribution to Lesson Planning, Critical Thinking and Openness in Teacher Education. Education Sciences, 13(10), 998. https://doi.org/10.3390/educsci13100998
  28. Haddaway, N. R., Collins, A. M., Coughlin, D., & Kirk, S. (2015). The Role of Google Scholar in Evidence Reviews and Its Applicability to Grey Literature Searching. PLOS ONE, 10(9), e0138237. https://doi.org/10.1371/journal.pone.0138237
  29. Hamilton, E. R., Rosenberg, J. M., & Akcaoglu, M. (2016). The Substitution Augmentation Modification Redefinition (SAMR) Model: a Critical Review and Suggestions for its Use. TechTrends, 60(5), 433–441. https://doi.org/10.1007/s11528-016-0091-y
  30. Jyothy, S. N., Kolil, V. K., Raman, R., & Achuthan, K. (2024). Exploring large language models as an integrated tool for learning, teaching, and research through the Fogg Behavior Model: a comprehensive mixed-methods analysis. Cogent Engineering, 11(1). https://doi.org/10.1080/23311916.2024.2353494
  31. Kanaki, K., & Kalogiannakis, M. (2023). Sample design challenges: an educational research paradigm. International Journal of Technology Enhanced Learning, 15(3), 266–285. https://doi.org/10.1504/ijtel.2023.131865
  32. Karataş, F., & Yüce, E. (2024). AI and the Future of Teaching: Preservice Teachers’ Reflections on the Use of Artificial Intelligence in Open and Distributed Learning. The International Review of Research in Open and Distributed Learning, 25(3), 304–325. https://doi.org/10.19173/irrodl.v25i3.7785
  33. Khalaf, B. K. (2018). Traditional and Inquiry-Based Learning Pedagogy: A Systematic Critical Review. International Journal of Instruction, 11(4), 545–564. https://doi.org/10.12973/iji.2018.11434a
  34. Kind, V. (2009). Pedagogical content knowledge in science education: perspectives and potential for progress. Studies in Science Education, 45(2), 169–204. https://doi.org/10.1080/03057260903142285
  35. Kohnke, L., & Zou, D. (2025). Artificial Intelligence Integration in TESOL Teacher Education: Promoting a Critical Lens Guided by TPACK and SAMR. TESOL Quarterly. Portico. https://doi.org/10.1002/tesq.3396
  36. Kohnke, L., Zou, D., & Su, F. (2025). Exploring the potential of GenAI for personalised English teaching: Learners’ experiences and perceptions. Computers and Education: Artificial Intelligence, 8, 100371. https://doi.org/10.1016/j.caeai.2025.100371
  37. Kotsis, K. T. (2024). Integrating ChatGPT into the Inquiry-Based Science Curriculum for Primary Education. European Journal of Education and Pedagogy, 5(6), 28–34. https://doi.org/10.24018/ejedu.2024.5.6.891
  38. Küchemann, S., Steinert, S., Revenga, N., Schweinberger, M., Dinc, Y., Avila, K. E., & Kuhn, J. (2023). Can ChatGPT support prospective teachers in physics task development? Physical Review Physics Education Research, 19(2). https://doi.org/10.1103/physrevphyseducres.19.020128
  39. Lee, G.-G., & Zhai, X. (2024). Using ChatGPT for Science Learning: A Study on Pre-service Teachers’ Lesson Planning. IEEE Transactions on Learning Technologies, 17, 1643–1660. https://doi.org/10.1109/tlt.2024.3401457
  40. Marangio, K., Carpendale, J., Cooper, R., & Mansfield, J. (2023). Supporting the Development of Science Pre-service Teachers’ Creativity and Critical Thinking in Secondary Science Initial Teacher Education. Research in Science Education, 54(1), 65–81. https://doi.org/10.1007/s11165-023-10104-x
  41. Markos, A., Prentzas, J., & Sidiropoulou, M. (2024). Pre-Service Teachers’ Assessment of ChatGPT’s Utility in Higher Education: SWOT and Content Analysis. Electronics, 13(10), 1985. https://doi.org/10.3390/electronics13101985
  42. 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
  43. Moundridou, M., Matzakos, N., & Doukakis, S. (2024). Generative AI tools as educators’ assistants: Designing and implementing inquiry-based lesson plans. Computers and Education: Artificial Intelligence, 7, 100277. https://doi.org/10.1016/j.caeai.2024.100277
  44. Mukhamejanova, D., Zhaksybay, M., & Kuchumova, G. (2025). Research-based teacher education in Kazakhstan: student perspectives on educational research methods courses. Journal of Education for Teaching, 51(3), 581–595. https://doi.org/10.1080/02607476.2025.2503008
  45. Anderson, J. E., Nguyen, C. A., & Moreira, G. (2025). Generative AI-driven personalization of the Community of Inquiry model: enhancing individualized learning experiences in digital classrooms. The International Journal of Information and Learning Technology, 42(3), 296–310. https://doi.org/10.1108/ijilt-10-2024-0240
  46. Nilsson, P., & Vikström, A. (2015). Making PCK Explicit—Capturing Science Teachers’ Pedagogical Content Knowledge (PCK) in the Science Classroom. International Journal of Science Education, 37(17), 2836–2857. https://doi.org/10.1080/09500693.2015.1106614
  47. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
  48. Peikos, G., & Stavrou, D. (2025). ChatGPT for Science Lesson Planning: An Exploratory Study Based on Pedagogical Content Knowledge. Education Sciences, 15(3), 338. https://doi.org/10.3390/educsci15030338
  49. Pellas, N. (2025). The Impact of AI-Generated Instructional Videos on Problem-Based Learning in Science Teacher Education. Education Sciences, 15(1), 102. https://doi.org/10.3390/educsci15010102
  50. De La Peña, S. I., & Maatubang, P. I. (2025). Inquiry-Based Science Using Five (5) E’s Instructional Model: A Case Study. European Journal of Contemporary Education and E-Learning, 3(2), 171–178. https://doi.org/10.59324/ejceel.2025.3(2).15
  51. Pernaa, J., Ikävalko, T., Takala, A., Vuorio, E., Pesonen, R., & Haatainen, O. (2024). Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis. Informatics, 11(2), 20. https://doi.org/10.3390/informatics11020020
  52. Petousi, V., & Sifaki, E. (2020). Contextualising harm in the framework of research misconduct. Findings from discourse analysis of scientific publications. International Journal of Sustainable Development, 23(3/4), 149. https://doi.org/10.1504/ijsd.2020.115206
  53. Prentzas, J., & Sidiropoulou, M. (2023). Assessing the Use of Open AI Chat-GPT in a University Department of Education. 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), 1–4. https://doi.org/10.1109/iisa59645.2023.10345910
  54. Qiao, C., Chen, Y., Guo, Q., & Yu, Y. (2024). Understanding science data literacy: a conceptual framework and assessment tool for college students majoring in STEM. International Journal of STEM Education, 11(1). https://doi.org/10.1186/s40594-024-00484-5
  55. Ragab, K. (2025). Empowering STEM Education With AI and Prompt Engineering. Prompt Engineering and Generative AI Applications for Teaching and Learning, 175–188. https://doi.org/10.4018/979-8-3693-7332-3.ch011
  56. Ramnarain, U., Ogegbo, A. A., Penn, M., Ojetunde, S., & Mdlalose, N. (2024). Pre-Service Science Teachers’ Intention to use Generative Artificial Intelligence in Inquiry-Based Teaching. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10159-z
  57. Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024). Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment. Education Sciences, 14(1), 83. https://doi.org/10.3390/educsci14010083
  58. Koehler, M. J., Mishra, P., & Cain, W. (2013). What is Technological Pedagogical Content Knowledge (TPACK)? Journal of Education, 193(3), 13–19. https://doi.org/10.1177/002205741319300303
  59. Shailendra, S., Kadel, R., & Sharma, A. (2024). Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education. IEEE Transactions on Education, 67(5), 777–785. https://doi.org/10.1109/te.2024.3432101
  60. Sotiropoulos, D., & Kalogiannakis, M. (2025). Using GenAI Tools to Enhance Teaching and Learning in Science. In Proceedings of the 14th Hybrid Edition of the International Conference New Perspectives in Science Education, 293-301, Italy-Florence: Filodiritto-Pixel, 19-21 March 2025. https://conference.pixel-online.net
  61. Stinken-Rösner, L., Hofer, E., Rodenhauser, A., & Abels, S. (2023). Technology Implementation in Pre-Service Science Teacher Education Based on the Transformative View of TPACK: Effects on Pre-Service Teachers’ TPACK, Behavioral Orientations and Actions in Practice. Education Sciences, 13(7), 732. https://doi.org/10.3390/educsci13070732
  62. Jin, F., Peng, X., Sun, L., Song, Z., Zhou, K., & Lin, C. (2025). Knowledge (Co‐)Construction Among Artificial Intelligence, Novice Teachers, and Experienced Teachers in an Online Professional Learning Community. Journal of Computer Assisted Learning, 41(2). Portico. https://doi.org/10.1111/jcal.70004
  63. Tenberga, I., & Daniela, L. (2024). Artificial Intelligence Literacy Competencies for Teachers Through Self-Assessment Tools. Sustainability, 16(23), 10386. https://doi.org/10.3390/su162310386
  64. Tseng, J.-J., Chai, C. S., Tan, L., & Park, M. (2020). A critical review of research on technological pedagogical and content knowledge (TPACK) in language teaching. Computer Assisted Language Learning, 35(4), 948–971. https://doi.org/10.1080/09588221.2020.1868531
  65. Uğraş, H., Uğraş, M., Papadakis, S., & Kalogiannakis, M. (2024). ChatGPT-Supported Education in Primary Schools: The Potential of ChatGPT for Sustainable Practices. Sustainability, 16(22), 9855. https://doi.org/10.3390/su16229855
  66. Valladares, L. (2021). Post-Truth and Education. Science & Education, 31(5), 1311–1337. https://doi.org/10.1007/s11191-021-00293-0
  67. Salinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024). Designing experiential learning activities with generative artificial intelligence tools for authentic assessment. Interactive Technology and Smart Education, 21(4), 708–734. https://doi.org/10.1108/itse-12-2023-0236
  68. Wang, K. D., Wu, Z., Tufts, L., Wieman, C., Salehi, S., & Haber, N. (2025). Scaffold or Crutch? Examining College Students’ Use and Views of Generative AI Tools for STEM Education. 2025 IEEE Global Engineering Education Conference (EDUCON), 1–10. https://doi.org/10.1109/educon62633.2025.11016406
  69. Wang, T., Ye, R., & Long, T. (2024). Exploring Pre-service Science Teachers’ Cognitive and Emotional Patterns in the Video-based Multimodal Analysis Integrated Collective Reflection: an Epistemic Network Analysis. 2024 International Symposium on Educational Technology (ISET), 416–420. https://doi.org/10.1109/iset61814.2024.00088
  70. 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
  71. Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
  72. Xu, Y., Liu, L., Xiong, J., & Zhu, G. (2025). Graders of the Future: Comparing the Consistency and Accuracy of Gpt4 and Pre-Service Teachers in Physics Essay Question Assessments. Journal of Baltic Science Education, 24(1), 187–207. Internet Archive. https://doi.org/10.33225/jbse/25.24.187
  73. Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850. https://doi.org/10.1038/s41562-024-02004-5
  74. Yazidi, R. E., & Rijal, K. (2024). Science Learning in the Context of “Indigenous Knowledge” for Sustainable Development. International Journal of Ethnoscience and Technology in Education, 1(1), 28. https://doi.org/10.33394/ijete.v1i1.10880
  75. Sarkar, M., Gutierrez-Bucheli, L., Yip, S. Y., Lazarus, M., Wright, C., White, P. J., Ilic, D., Hiscox, T. J., & Berry, A. (2024). Pedagogical content knowledge (PCK) in higher education: A systematic scoping review. Teaching and Teacher Education, 144, 104608. https://doi.org/10.1016/j.tate.2024.104608
  76. Yue, M., Jong, M. S.-Y., & Ng, D. T. K. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 29(15), 19505–19536. https://doi.org/10.1007/s10639-024-12621-2