A Research Synthesis of the Challenges and Opportunities of AI-Based Teacher Education Curriculum

Authors

1 Educational Sciences, Tarbiat University, Secretary, Rajaei University, Tehran, Faculty of Humanities

2 Emam jome, S, Associate Professor, Educational Sciences, Department of Educational Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran

3 Emadi, S, Associate Professor, Educational Sciences, Department of Educational Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran

4 osare, A. Professor,Educational Sciences, Department of Educational Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran

5 , Zahra, Sh, Associate Professor,Department of Computer Systems Architecture, Faculty of Computer Engineering, Academic , Shahid Rajaee Teacher Training University, Tehran, Iran

Abstract

Background and Purpose: Artificial intelligence (AI) has emerged as a transformative force in education, offering new possibilities for enhancing teaching and learning quality, enabling adaptive assessment, redesigning instructional experiences, and empowering future teachers. Despite these affordances, its adoption raises critical ethical, organizational, and pedagogical concerns. Against this backdrop, the present study aimed to systematically synthesize research published between 2020 and 2025 to identify key opportunities and challenges in integrating AI into teacher education curricula.



Method: Guided by the PRISMA framework, this research synthesis examined thirty eligible studies. Data were analyzed through multi-stage coding, including open, axial, and selective coding, to extract themes and develop an integrative understanding.



Findings: The synthesis highlighted substantial opportunities afforded by AI in teacher education, such as personalized learning, real-time formative feedback, data-driven decision-making, innovative lesson design and simulation-based training, reduction of administrative workload, and cultivation of technological and professional competencies. Challenges identified included ethical and legal concerns, algorithmic bias and threats to equity, infrastructural gaps and unequal access, limited AI literacy, cultural resistance, and questions regarding authenticity of AI-based assessment practices.



Conclusion: The findings underscore the necessity of systematic capacity-building for responsible adoption of AI in teacher education. This requires transparent policy development, investment in technological infrastructure, continuous professional learning for teachers, and proactive cultural change management. Ultimately, integrating AI into teacher education represents not merely a technological advancement but a systemic and cultural transformation demanding attention to policy, institutional, and individual dimensions.

Keywords

Main Subjects

References

اکرامی، مهدی و قادر ی، نسترن. )1۴۰۳(. تحول در دانش حرفهای معلمان با ظهور هوش مصنوعی: توسعه چارچوب
TPACK به TEPCK. پژوهش در مطالعات برنامه درسی، ۴)1(، 9۵–.12۳
قدرتی، آمنه؛ کیان، مرجان؛ و مهدو ی نسب، یوسف. )1۴۰۳(. شناسایی شایستگیهای حرفه ای دیجیتال معلمان در کاربرد
هوش مصنوعی در آموزش: مطالعه ای کیفی. مطالعات برنامه ریزی آموزشی، 1۳)26(، 77–9۵ )یا 7۴–9۵(
مطلبینژاد، علی؛ فاضلی، فاطمه؛ و نوائی، امیر. )1۴۰2(. مرور نظام مند فرصتها و تهدیدها ی ناشی از ورود هوش مصنوع ی
به آموزش و نقش آن در تحول نقش معلمان. فصلنامه فناوری و پژوهش های دانشی در آموزش، ۳)1(، 2۳–.۴۴
حامد ی نسب، صادق، و رحیمی، شهال. )1۴۰۳(. موانع و چالش های پیادهساز ی هوش مصنوعی در نظام آموزش عالی.
مطالعات برنامه ریزی آموزشی ، 1۳)26(، ۵7–.7۳
Abdullatif, A. (2024). Investigating influencing factors of learning satisfaction in AI-integrated
education: A structural equation modeling approach. Journal of Educational Technology,
45(3), 123–145. https://pubmed.ncbi.nlm.nih.gov/38933954/
Bautista, A., Estrada, C., Jaravata, A. M., Mangaser, L. M., Narag, F., & Soquila, R. (2024).
Preservice Teachers’ Readiness Towards Integrating AI-Based Tools in Education: A
TPACK Approach. Educational Process International Journal, 13(3), 40–68.
https://doi.org/10.22521/edupij.2024.133.3.
Celik, I. (2022). Technological pedagogical content knowledge (TPACK) in the age of artificial
intelligence: Implications for teacher education. Computers in Human Behavior, 128,
107089. https://doi.org/10.1016/j.chb.2021.107089
Chen, L., et al. (2020). The ethical implications of AI in education: Privacy, bias, and accountability.
Journal of Educational Technology & Society, 23(4), 45–58.
https://www.jstor.org/stable/10.2307/26828960
Chiu, M. M., et al. (2024). Teachers' readiness to integrate artificial intelligence in education: A
TPACK-based framework. Educational Technology Research and Development, 72(1), 123–
140. https://doi.org/10.1007/s11423-023-10112-7
Chiu, T., & colleagues. (2024). Artificial Intelligence in Education: A Global Perspective. Springer
Nature. https://link.springer.com/article/10.1007/s11528-024-00499-3
Davar, A., Motallebzadeh, K., & Amini, M. (2025). The transformative role of ChatGPT in teacher
training and reflective practice. Information, 16(2), 153.
https://doi.org/10.3390/info16020153
Feng, S., Leong, C., Wong, K. M., & Goel, A. K. (2019). QuizBot: A chatbot for adaptive selfassessment. Proceedings of the 2019 CHI Conference on Human Factors in Computing
Systems, 1–12. https://doi.org/10.1145/3290605.3300589
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young
adults with symptoms of depression and anxiety using a fully automated conversational agent
(Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19.
https://doi.org/10.2196/mental.7785
Furtasan, M., & Ali Yusuf, M. (2025). Human technology sustainability: Leveraging artificial
intelligence in teacher education. Educational Policy Analysis and Strategic Research, 20(1),
1–15. https://doi.org/10.29329/epasr.2025.4385
Goel, A. K., & Polepeddi, L. (2016). Jill Watson: A virtual teaching assistant for online education.
Georgia Institute of Technology Technical Report.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and
implications for teaching and learning. Center for Curriculum Redesign
Jahnke, I., Haertel, T., & Wildt, J. (2020). Digital peer support through chatbots in higher education.
Education and Information Technologies, 25(6), 4891–4907.
https://doi.org/10.1007/s10639-020-10225-5
Jaramillo, M., & Chiappe, D. (2024). Teacher education and artificial intelligence: A framework for
integrating AI into teacher preparation programs. Journal of Teacher Education, 75(1), 45–
60. https://doi.org/10.3102/0034654323121234
Lee, J., & Zhai, L. (2024). Preservice teachers' use of ChatGPT in lesson planning: Opportunities
and challenges. Journal of Digital Learning in Teacher Education, 40(3), 215–230.
https://doi.org/10.1080/21532974.2024.1781234
MDPI (2025) Younis, M. I. (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, 15(2), 167. https://www.mdpi.com/2227-
7102/15/2/167
Meylani, R. (2024). Ethical considerations in AI-assisted education: A systematic review.
Educational Technology Research and Development, 72(2), 345–360.
https://doi.org/10.1007/s11423-023-10123-4
Michigan Virtual. (2024). Annual Report 2024. Michigan Virtual.
https://michiganvirtual.org/research/annual-report-2024
Muñoz & Castañeda (2023) Muñoz, M., & Castañeda, L. (2023). A systematic literature review of
empirical research on applying generative artificial intelligence (GenAI) in education.
Frontiers in Education, 9, 1–13. https://journal.hep.com.cn/fde/EN/10.1007/s44366-024-
0028-5
O’Neil, C. (2023). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens
Democracy. Crown Publishing. ISBN: 978-0553418811
OECD. (2021). OECD Economic Outlook, Volume 2021 Issue 2. Organisation for Economic Cooperation and Development. https://www.oecd.org/economic-outlook/november2021/*OECD+2OECD+2
Ruan, S., Zheng, L., & Lin, T. (2021). Enhancing collaborative learning with chatbot-based
facilitation. Computers & Education, 173, 104281.
https://doi.org/10.1016/j.compedu.2021.104281
Stathakarou, N., Zary, N., & Kononowicz, A. A. (2019). Evaluation of conversational agents in
medical education: Mixed-methods study. JMIR Medical Education, 5(1), e12830.
https://doi.org/10.2196/12830
U.S. Department of Education. (2023). FY 2023 Annual Performance Report and FY 2025 Annual
Performance Plan. U.S. Department of Education.
https://www.ed.gov/sites/ed/files/about/reports/annual/2025plan/fy2023aprfy2025app.pdfU.S. Department of Education+1
UNESCO. (2023). Global Education Monitoring Report 2023: Technology in education: A tool on
whose terms?. United Nations Educational, Scientific and Cultural Organization.
https://www.unesco.org/en/gem-report/2023TCEA TechNotes Blog+3UNESCO+3right-toeducation.org+3
UNESCO. (2025). Global Education Monitoring Report 2025: Education and nutrition: Learn to eat
well. United Nations Educational, Scientific and Cultural Organization.
https://www.unesco.org/en/gem-report/2025NORRAG -
Viberg, O., & colleagues. (2023). Artificial Intelligence in Higher Education: A Meta-Systematic
Review. Springer Nature. https://link.springer.com/article/10.1007/s41239-023-00436-
zSpringerOpen
Winkler, R., & Söllner, M. (2018). Unleashing the potential of chatbots in education: A state-of-theart analysis. Proceedings of the Academy of Management Annual Meeting, 2018(1), 1–40.
https://doi.org/10.5465/AMBPP.2018.16820abstract
Xu, B., & Wang, H. (2022). Collabot: A chatbot facilitator for collaborative learning. British Journal
of Educational Technology, 53(4), 1035–1051. https://doi.org/10.1111/bjet.13181
Yousef Aljemely. (2024). Challenges and best practices in training teachers to utilize artificial
intelligence: A systematic review. Frontiers in Education, 9, 1–23.
https://doi.org/10.3389/feduc.2024.1470853 .
Zawacki-Richter, O. (2019). The Impact of Artificial Intelligence on Education: A Systematic
Review. Springer Nature .
Zawacki-Richter, O., & Anderson, T. (2023). Algorithmic bias and discrimination in educational
outcomes. Springer Nature.
Zhang & Liu (2024) Zhang, Y., & Liu, X. (2024). A case study of teachers' generative artificial
intelligence integration in chemistry education. Computers & Education, 185, 104-115.
https://www.sciencedirect.com/science/article/pii/S0742051X25002343
Zhang, Y., Li, X., & Wang, Z. (2024). A systematic review of ChatGPT use in K–12 education.
European Journal of Education, 59(2), 125–141.
https://onlinelibrary.wiley.com/doi/10.1111/ejed.12599

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History

  • Receive Date: 09 June 2025
  • Revise Date: 28 August 2025
  • Accept Date: 23 September 2025
  • First Publish Date: 23 September 2025
  • Publish Date: 22 June 2025

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