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Generative AI for feedback and collaborative knowledge construction in preservice physics teacher education

Yuchen Jiang1, Xiu-Mei Feng1,*, Yuting Liu1, Yiting Wang1, Li Xie1,†, and Lei Bao2,‡

  • 1College of Physical Science and Technology, Central China Normal University, Wuhan, Hubei 430079, China
  • 2School of Biological Science and Medical Engineering, Research Center for Learning Science, Key Laboratory of Child Development and Learning Science (MOE), Southeast University, Nanjing 211189, China

  • *Contact author: xiumeifeng@mail.ccnu.edu.cn
  • †Contact author: shirlyxieli@ccnu.edu.cn
  • ‡Contact author: lei.bao@outlook.com
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Phys. Rev. Phys. Educ. Res. 22, 010116 – Published 6 February, 2026

DOI: https://doi.org/10.1103/hm13-jv98

Abstract

Generative artificial intelligence (GenAI) offers strong potential to transform teaching practices; however, empirical research on its classroom effectiveness remains limited. This study explores how GenAI can be integrated into preservice physics teacher training by replacing two traditional instructor roles in an instructional design course: providing evaluative feedback and facilitating collaboration. A GenAI-based assistant was developed to assess instructional designs according to predefined criteria and to provide personalized feedback. A total of 110 preservice physics teachers received feedback from one of the three sources: a teaching expert, a teaching assistant, or GenAI to revise their instructional designs. They subsequently collaborated with GenAI for further refinement. Results indicate that GenAI evaluations closely aligned with those of human experts and teaching assistants. In blinded evaluations, preservice physics teachers rated GenAI feedback as comparable to human feedback. Furthermore, collaboration with GenAI resulted in significant improvements in the overall quality of instructional design. This progress was particularly most evident in the structured, content-expandable components. While these findings demonstrate GenAI’s feasibility from a functional perspective, challenges related to preservice teachers’ perceptions also emerged. Although design quality improved through collaboration with GenAI, independent design skills, measured by a final paper-and-pencil test, showed no similar improvement. Preservice physics teachers acknowledged the need to filter and revise GenAI-generated content, but often directly adopted its suggestions due to the convenience of GenAI. Notably, feedback was rated lower once participants realized it was AI-generated, even if the actual source was not, revealing trust-related concerns. This study moves beyond technical evaluations of GenAI, offering insights into the pedagogical challenges and limitations that arise when GenAI assumes the roles that are traditionally performed by human educators.

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References (62)

  1. G. Cooper, Examining science education in ChatGPT: An exploratory study of generative artificial intelligence, J. Sci. Educ. Technol. 32, 444 (2023).
  2. Y. Feldman-Maggor, R. Blonder, and G. Alexandron, Perspectives of generative AI in chemistry education within the TPACK framework, J. Sci. Educ. Technol. 34, 1 (2025).
  3. J. A. Bowen and C. E. Watson, Teaching with AI: A Practical Guide to a New Era of Human Learning (Johns Hopkins University Press, Baltimore, Maryland, 2024).
  4. M. Bower, J. Torrington, J. W. M. Lai, P. Petocz, and M. Alfano, How should we change teaching and assessment in response to increasingly powerful generative artificial intelligence? Outcomes of the ChatGPT teacher survey, Educ. Inf. Technol. 0 (2024).
  5. S. A. D. Popenici and S. Kerr, Exploring the impact of artificial intelligence on teaching and learning in higher education, Res. Pract. Technol. Enhanced Learn. 12, 22 (2017).
  6. H. Jang and H. Choi, A double-edged sword: Physics educators’ perspectives on utilizing ChatGPT and its future in classrooms, J. Sci. Educ. Technol. 34, 267 (2025).
  7. I. Celik, M. Dindar, H. Muukkonen, and S. Järvelä, The promises and challenges of artificial intelligence for teachers: A systematic review of research, TechTrends 66, 616 (2022).
  8. S. Küchemann, S. Steinert, N. Revenga, M. Schweinberger, Y. Dinc, K. E. Avila, and J. Kuhn, Can ChatGPT support prospective teachers in physics task development?, Phys. Rev. Phys. Educ. Res. 19, 020128 (2023).
  9. J. S. Jauhiainen and A. Garagorry Guerra, Generative AI in education: ChatGPT-4 in evaluating students’ written responses, Innovation Educ. Teach. Int. 62, 1377 (2025).
  10. A. V. Y. Lee, Supporting students’ generation of feedback in large-scale online course with artificial intelligence-enabled evaluation, Stud. Educ. Eval. 77, 101250 (2023).
  11. T. Wan and Z. Chen, Exploring generative AI assisted feedback writing for students’ written responses to a physics conceptual question with prompt engineering and few-shot learning, Phys. Rev. Phys. Educ. Res. 20, 010152 (2024).
  12. I.-A. Chounta, E. Bardone, A. Raudsep, and M. Pedaste, Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K-12 education, Int. J. Artif. Intell. Educ. 32, 725 (2022).
  13. G. Cooper, K.-S. Tang, and A. Fitzgerald, Intersections of mind and machine: Navigating the nexus of artificial intelligence, science education, and the preparation of pre-service teachers, J. Sci. Educ. Technol. 34, 1255 (2025).
  14. R. Hall, Generative AI and re-weaving a pedagogical horizon of social possibility, Int. J. Educ. Technol. High. Educ. 21, 12 (2024).
  15. R. Blonder, Y. Feldman-Maggor, and S. Rap, Are they ready to teach? Generative AI as a means to uncover pre-service science teachers’ PCK and enhance their preparation program, J. Sci. Educ. Technol. 34, 1301 (2024).
  16. P. MacDowell, K. Moskalyk, K. Korchinski, and D. Morrison, Preparing educators to teach and create with generative artificial intelligence, Can. J. Learn. Tech. 50, 1 (2024).
  17. S. Duan, M. Exter, and Q. Li, In their ideal future, are preservice teachers willing to integrate technology in their teaching and why?, TechTrends 68, 734 (2024).
  18. Q. Tan, Reimagining teacher development in the era of generative AI: A scoping review, Teach. Teach. Educ. 168, 105236 (2025).
  19. R. F. Kizilcec, E. Huber, E. C. Papanastasiou, A. Cram, C. A. Makridis, A. Smolansky, S. Zeivots, and C. Raduescu, Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States, Comput. Educ. Artif. Intell. 7, 100269 (2024).
  20. S. G. Garofalo and S. J. Farenga, Science teacher perceptions of the state of knowledge and education at the advent of generative artificial intelligence popularity, Sci. Educ. 34, 893 (2025).
  21. X. Zhai, Transforming teachers’ roles and agencies in the era of generative AI: Perceptions, acceptance, knowledge, and practices, J. Sci. Educ. Technol. 34, 1323 (2024).
  22. X. Zhai, M. Nyaaba, and W. Ma, Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science?, Sci. Educ. 34, 649 (2025).
  23. R. Zhang, X. Liu, Y. Yang, J. Tripp, and B. Shao, Preservice science teachers’ instructional design competence: Characteristics and correlations, Eurasia J. Math. Sci. Technol. Educ. 14, 1075 (2018).
  24. T. J. Gurl, M. P. Markinson, and A. F. Artzt, Using ChatGPT as a lesson planning assistant with preservice secondary mathematics teachers, Digital Exp. Math. Educ. 11, 114 (2025).
  25. S. G. Magliaro and N. Shambaugh, Student models of instructional design, Educ. Technol. Res. Dev. 54, 83 (2006).
  26. W. B. Rouse and N. M. Morris, On looking into the black box: Prospects and limits in the search for mental models, Psychol. Bull. 100, 349 (1986).
  27. M. Molenda, C. M. Reigeluth, and L. M. Nelson, Instructional design, in Encyclopedia of Cognitive Science, 1st ed., edited by L. Nadel (Wiley, New York, 2006).
  28. J. E. Kemp, The Instructional Design Process (HarperCollins Publishers, New York, NY, 1985).
  29. P. L. Smith and T. J. Ragan, Instructional Design (John Wiley & Sons, New York, 2004).
  30. W. Dick, L. Carey, and J. O. Carey, The Systematic Design of Instruction (Scott Foresman, Glenview, IL, 1985).
  31. C.-C. Liu, H.-J. Wang, D. Wang, Y.-F. Tu, G.-J. Hwang, and Y. Wang, An interactive technological solution to foster preservice teachers’ theoretical knowledge and instructional design skills: A chatbot-based 5E learning approach, Interact. Learn. Environ. 32, 6698 (2024).
  32. M. C. Scheeler, K. McKinnon, and J. Stout, Effects of immediate feedback delivered via webcam and bug-in-ear technology on preservice teacher performance, Teach. Educ. Spec. Educ. 35, 77 (2012).
  33. G. Kortemeyer, J. Nöhl, and D. Onishchuk, Grading assistance for a handwritten thermodynamics exam using artificial intelligence: An exploratory study, Phys. Rev. Phys. Educ. Res. 20, 020144 (2024).
  34. J. Escalante, A. Pack, and A. Barrett, AI-generated feedback on writing: Insights into efficacy and ENL student preference, Int. J. Educ. Technol. Higher Educ. 20, 57 (2023).
  35. M. F. Shahzad, S. Xu, and H. Zahid, Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education, Educ. Inf. Technol. 30, 3691 (2025).
  36. G. Bilquise, S. Ibrahim, and S. M. Salhieh, Investigating student acceptance of an academic advising chatbot in higher education institutions, Educ. Inf. Technol. 29, 6357 (2024).
  37. F. Kehoe, Leveraging generative AI tools for enhanced lesson planning in initial teacher education at post primary, Ir. J. Technol. Enhanced Learn. 7, 172 (2023).
  38. C. K. Lo, What is the impact of ChatGPT on education? A rapid review of the literature, Educ. Sci. 13, 410 (2023).
  39. B. Hu, L. Zheng, J. Zhu, L. Ding, Y. Wang, and X. Gu, Teaching plan generation and evaluation with GPT-4: Unleashing the potential of LLM in instructional design, IEEE Trans. Learn. Technol. 17, 1445 (2024).
  40. M. R. Karaman and İ. Göksu, Are lesson plans created by ChatGPT more effective? An experimental study, Int. J. Technol. Des. Educ. 7, 107 (2024).
  41. M. Wang, M. Wang, X. Xu, L. Yang, D. Cai, and M. Yin, Unleashing ChatGPT’s power: A case study on optimizing information retrieval in flipped classrooms via prompt engineering, IEEE Trans. Learn. Technol. 17, 629 (2024).
  42. G. Kortemeyer, Tailoring chatbots for higher education: Some insights and experiences, arXiv:2409.06717.
  43. V. G. A. Hakim, N. A. Paiman, and M. H. S. Rahman, Genie-on-demand: A custom AI chatbot for enhancing learning performance, self-efficacy, and technology acceptance in occupational health and safety for engineering education, Comput. Appl. Eng. Educ. 32, e22800 (2024).
  44. J. Lademann, J. Henze, and S. Becker-Genschow, Augmenting learning environments using AI custom chatbots: Effects on learning performance, cognitive load, and affective variables, Phys. Rev. Phys. Educ. Res. 21, 010147 (2025).
  45. K.-S. Tang and G. B. S. Putra, Generative AI as a dialogic partner: Enhancing multiple perspectives, reasoning, and argumentation in science education with customized chatbots, J. Sci. Educ. Technol. (2025), 10.1007/s10956-025-10240-1.
  46. M. Abbas, F. A. Jam, and T. I. Khan, Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students, Int. J. Educ. Technol. Higher Educ. 21, 10 (2024).
  47. G. Van Den Berg and E. Du Plessis, ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education, Educ. Sci. 13, 998 (2023).
  48. G.-G. Lee and X. Zhai, Using ChatGPT for science learning: A study on pre-service teachers’ lesson planning, IEEE Trans. Learn. Technol. 17, 1643 (2024).
  49. P. Jia and C. Stan, Artificial intelligence factory, data risk, and VCs’ mediation: The case of ByteDance, an AI-powered startup, J. Risk Finance Manage. 14, 203 (2021).
  50. See Supplemental Material at http://link.aps.org/supplemental/10.1103/hm13-jv98 for the specific criteria, prompts, manual, questionnaire, and results of subscale analyses.
  51. M. N. Dahlkemper, S. Z. Lahme, and P. Klein, How do physics students evaluate artificial intelligence responses on comprehension questions? A study on the perceived scientific accuracy and linguistic quality of ChatGPT, Phys. Rev. Phys. Educ. Res. 19, 010142 (2023).
  52. R. Paul, The state of critical thinking today, New Dir. Community Coll. 2005, 27 (2005).
  53. S. K. Banihashem, N. T. Kerman, O. Noroozi, J. Moon, and H. Drachsler, Feedback sources in essay writing: Peer-generated or AI-generated feedback?, Int. J. Educ. Technol. High. Educ. 21, 23 (2024).
  54. T. K. Koo and M. Y. Li, A guideline of selecting and reporting intraclass correlation coefficients for reliability research, J. Chiropr. Med. 15, 155 (2016).
  55. J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (L. Erlbaum Associates, Hillsdale, NJ, 1988).
  56. D. Schiering, S. Sorge, M. M. Keller, and K. Neumann, A proficiency model for pre-service physics teachers’ pedagogical content knowledge (PCK)—What constitutes high-level PCK?, J. Res. Sci. Teach. 60, 136 (2023).
  57. W. Dai, J. Lin, H. Jin, T. Li, Y.-S. Tsai, D. Gašević, and G. Chen, Can large language models provide feedback to students? A case study on ChatGPT, in Proceedings of the 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (IEEE, New York, 2023), pp. 323–325.
  58. W. Chang and J. Park, A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set, J. Retail. Consum. Serv. 78, 103743 (2024).
  59. G. Kortemeyer and W. Bauer, Cheat sites and artificial intelligence usage in online introductory physics courses: What is the extent and what effect does it have on assessments?, Phys. Rev. Phys. Educ. Res. 20, 010145 (2024).
  60. N. Şimşek, Integration of ChatGPT in mathematical story-focused 5E lesson planning: Teachers, and pre-service teachers’ interactions with ChatGPT, Educ. Inf. Technol. 30, 11391 (2025).
  61. G. Kortemeyer, Could an artificial-intelligence agent pass an introductory physics course?, Phys. Rev. Phys. Educ. Res. 19, 010132 (2023).
  62. M. Bearman, J. Tai, P. Dawson, D. Boud, and R. Ajjawi, Developing evaluative judgement for a time of generative artificial intelligence, Assess. Eval. Higher Educ. 49, 893 (2024).

Outline

Information

Phys. Rev. Phys. Educ. Res. 22, 010116– Published 6 February, 2026
  • Received 4 November 2025
  • Accepted 26 January 2026
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