Applications of generative AI in early childhood education: A systematic review

Yuxin Zhang; Siti Hajar Binti Halili; Zamzami Zainuddin

doi:10.29333/ejmste/18068

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Yuxin Zhang ¹ , Siti Hajar Binti Halili ¹ ^* , Zamzami Zainuddin ²

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¹ Department of Curriculum and Instructional Technology, University of Malaya, Kuala Lumpur, MALAYSIA² College of Education, Psychology and Social Work, Flinders University, Adelaide, SA, AUSTRALIA^* Corresponding Author

Abstract

Generative artificial intelligence (Gen AI) has emerged as a topic of interest in education research. However, its applications in early childhood education (ECE) remain underexplored. This systematic review synthesizes empirical evidence on Gen AI in ECE, organizing findings by stakeholder perspective: young children, families, and teachers. Following PRISMA 2020 guidelines, 29 studies published between December 2022 and July 2025 were identified from eight databases. Results show that publications increased sharply from one in 2023 to 16 in 2024, with research concentrated in the USA and China. Gen AI supports ECE through enhanced learning outcomes, personalized learning, increased engagement, parent support, and teacher efficiency. However, these benefits were consistently dependent on active adult mediation. Challenges include technical limitations, content quality issues, and accuracy concerns. The findings suggest that Gen AI is best positioned as a complement to human guidance rather than a replacement. Future research should employ longitudinal designs and expand geographic diversity.

Keywords

generative AI
early childhood education
education technology
systematic review

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

EURASIA J Math Sci Tech Ed, Volume 22, Issue 3, March 2026, Article No: em2793

https://doi.org/10.29333/ejmste/18068

Publication date: 11 Mar 2026

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References

Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), Article 296. https://doi.org/10.3390/info16040296
Bramer, W. M., De Jonge, G. B., Rethlefsen, M. L., Mast, F., & Kleijnen, J. (2018). A systematic approach to searching: An efficient and complete method to develop literature searches. Journal of the Medical Library Association, 106(4), 531-541. https://doi.org/10.5195/jmla.2018.283
Chin, J. H., Lee, S., Ashraf, M., Zago, M., Xie, Y., Wolfgram, E. A., Yeh, T., & Kim, P. (2024). Young children’s creative storytelling with ChatGPT vs. parent: Comparing interactive styles. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-7). ACM. https://doi.org/10.1145/3613905.3650770
Copple, C., & Bredekamp, S. (Eds). (2009). Developmentally appropriate practice in early childhood programs serving children from birth through age 8 (3rd ed.). National Association for the Education of Young Children.
Daniel, K., Msambwa, M. M., & Wen, Z. (2025). Can generative AI revolutionise academic skills development in higher education? A systematic literature review. European Journal of Education, 60(1), Article e70036. https://doi.org/10.1111/ejed.70036
Dietz Smith, G., Prasad, S., Davidson, M. J., Findlater, L., & Shapiro, R. B. (2024). Contextq: Generated questions to support meaningful parent-child dialogue while co-reading. In Proceedings of the 23^rd Annual ACM Interaction Design and Children Conference (pp. 408-423). ACM. https://doi.org/10.1145/3628516.3655809
Edwards, C. P., & Gandini, L. (2018). The Reggio Emilia approach to early childhood education. In J. L. Roopnarine, J. E. Johnson, S. Flannery Quinn, & M. M. Patte (Eds.), Handbook of international perspectives on early childhood education (pp. 365-378). Routledge. https://doi.org/10.4324/9781315562193-26
Fuchs, K. (2023). Exploring the opportunities and challenges of NLP models in higher education: Is Chat GPT a blessing or a curse? Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1166682
He, K., Liu, X., Xu, Y., Bustamante, A. S., & Warschauer, M. (2025a). “Carlitos the curious caterpillar”: Exploring teacher-AI co-creation of culturally responsive educational materials for young learners. In Proceedings of the 24^th Interaction Design and Children (pp. 236-254). ACM. https://doi.org/10.1145/3713043.3727056
He, K., Gastón-Panthaki, A., Zhuo, D., Munsey, J., Zhang, M., & Warschauer, M. (2025b). StoryPal: Supporting Young children’s dialogic reading with large language models. In Proceedings of the 24^th Interaction Design and Children (pp. 494-511). https://doi.org/10.1145/3713043.3728867
Ho, H.-R., Kargeti, N., Liu, Z., & Mutlu, B. (2025). SET-PAiREd: Designing for parental involvement in learning with an AI-assisted educational robot. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-20). ACM. https://doi.org/10.1145/3706598.3713330
Lee, J., Yoon, S., Lee, K., Jeong, E., Cho, J.-E., Park, W., Yim, D., & Hwang, I. (2024). Open sesame? Open salami! Personalizing vocabulary assessment-intervention for children via pervasive profiling and bespoke storybook generation. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-32). ACM. https://doi.org/10.1145/3613904.3642580
Lee, Y., Kim, T. S., Kim, S., Yun, Y., & Kim, J. (2023). Dapie: Interactive step-by-step explanatory dialogues to answer children’s why and how questions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-22). ACM. https://doi.org/10.1145/3544548.3581369
Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning, 10, Article 29. https://doi.org/10.1038/s41539-025-00320-7
Li, Z., Thomas, T., Yu, C.-L., & Xu, Y. (2024). ‘I said knight, not night!’: Children’s communication breakdowns and repairs with AI versus human partners. In Proceedings of the 23^rd Annual ACM Interaction Design and Children Conference (pp. 781-788). ACM. https://doi.org/10.1145/3628516.3659394
Lillard, A. S. (2017). Montessori: The science behind the genius (3rd ed.). Oxford University Press.
Liu, D., Zhou, H., & An, P. (2024). ‘When he feels cold, he goes to the seahorse’—Blending generative AI into multimaterial storymaking for family expressive arts therapy. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-21). ACM. https://doi.org/10.1145/3613904.3642852
Liu, X., Xing, Y., Chen, Y., Pei, W., Choi, J., & Xu, Y. (2025). Children’s narrative participation in story co-creation with AI vs. Human partners: Response styles and narrative features. In Proceedings of the 24^th Interaction Design and Children (pp. 803-809). ACM. https://doi.org/10.1145/3713043.3731486
Liu, Y., Zha, S., Zhang, Y., Wang, Y., Zhang, Y., Xin, Q., Nie, L. Y., Zhang, C., & Xu, Y. (2025). BrickSmart: Leveraging generative AI to support children’s spatial language learning in family block play. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-19). ACM. https://doi.org/10.1145/3706598.3714212
Lu, R.-S., Lin, H.-C. K., Yang, Y.-C., & Chen, Y.-P. (2024). Integrating urban mining concepts through ai-generated storytelling and visuals: Advancing sustainability education in early childhood. Sustainability, 16(24), Article 11304. https://doi.org/10.3390/su162411304
Lyu, Y., Liu, D., An, P., Tong, X., Zhang, H., Katsuragawa, K., & Zhao, J. (2024). EMooly: Supporting autistic children in collaborative social-emotional learning with caregiver participation through interactive AI-infused and AR activities. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (pp. 1-36). ACM. https://doi.org/10.1145/3699738
Merino-Campos, C. (2025). The impact of artificial intelligence on personalized learning in higher education: A systematic review. Trends in Higher Education, 4(2), Article 17. https://doi.org/10.3390/higheredu4020017
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, 372, Article n71. https://doi.org/10.1136/bmj.n71
Perifanou, M., & Economides, A. A. (2025). Collaborative uses of genAI tools in project-based learning. Education Sciences, 15(3), Article 354. https://doi.org/10.3390/educsci15030354
Rakap, S. (2024). Chatting with GPT: Enhancing individualized education program goal development for novice special education teachers. Journal of Special Education Technology, 39(3), 339-348. https://doi.org/10.1177/01626434231211295
Rakap, S., & Balikci, S. (2024). Enhancing IEP Goal development for preschoolers with autism: A preliminary study on ChatGPT integration. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-024-06343-0
Seiradakis, E. V. (2023). Unpacking experts’ opinions on ChatGPT potential assistive roles and risks in early childhood special education. In Proceedings of the International Conference on New Media Pedagogy (pp. 380-392). Springer. https://doi.org/10.1007/978-3-031-63235-8_25
Sharma, R. C., & Panja, S. K. (2025). Addressing academic dishonesty in higher education: A systematic review of generative AI’s impact. Open Praxis, 17(2), 251-269. https://doi.org/10.55982/openpraxis.17.2.820
Shen, J. J., King Chen, J., Findlater, L., & Dietz Smith, G. (2025). eaSEL: Promoting social-emotional learning and parent-child interaction through AI-mediated content consumption. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-18). ACM. https://doi.org/10.1145/3706598.3713405
Su, J., & Yang, W. (2024). Powerful or mediocre? Kindergarten teachers’ perspectives on using ChatGPT in early childhood education. Interactive Learning Environments, 32(10), 6496-6508. https://doi.org/10.1080/10494820.2023.2266490
Sun, M., Yan, R., & Wen, R. (2025). Generative AI in Chinese early childhood education: Teachers’ usage patterns, perceptions, and factors influencing pedagogical applications. International Journal of Teacher Education and Professional Development, 8(1), 1-23. IEEE. https://doi.org/10.4018/IJTEPD.382379
Sun, S., Wang, Z., Zhang, J., Zhao, X., & Qiao, X. (2024). StoryChat: An interactive storytelling system for engaging with agent characters in a story. In Proceedings of the 2024 17^th International Symposium on Computational Intelligence and Design (pp. 213-217). https://doi.org/10.1109/ISCID63852.2024.00055
Sun, Y., Chen, J., Yao, B., Liu, J., Wang, D., Ma, X., Lu, Y., Xu, Y., & He, L. (2024). Exploring parent’s needs for children-centered AI to support preschoolers’ interactive storytelling and reading activities. In Proceedings of the ACM on Human-Computer Interaction (pp. 1-25). ACM. https://doi.org/10.1145/3687035
United Nations Children’s Fund. (2023). Early childhood development: UNICEF vision for every child. UNICEF. https://doi.org/10.18356/9789213585429
Vella, K., Dobson, M., & Brereton, M. (2025). ‘Hello, Mr tree’: Toying with playful conversational AI in the early years. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-6). ACM. https://doi.org/10.1145/3706599.3719878
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wang, Z., Pan, J., Jin, D., Zhang, J., Cao, J., Zhang, C., Li, Z., Hansen, P., Zhao, Y., Sun, S., & Qiao, X. (2025). CharacterCritique: Supporting children’s development of critical thinking through multi-agent interaction in story reading. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-21). ACM. https://doi.org/10.1145/3706598.3713602
Wong, J. M. S., Liu, P. H. C., & Yip, C. W. H. (2024). Navigating the digital shift: Early childhood educators’ attitudes towards generative artificial intelligence and emerging technologies. In Proceedings of the Asia Education Technology Symposium (pp. 87-97). Springer. https://doi.org/10.1007/978-981-96-4952-5_6
Xu, Y., Thomas, T., Yu, C.-L., & Pan, E. Z. (2025). What makes children perceive or not perceive minds in generative AI? Computers in Human Behavior: Artificial Humans, 4, Article 100135. https://doi.org/10.1016/j.chbah.2025.100135
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
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, Article 21. https://doi.org/10.1186/s41239-024-00453-6
Zhang, C., Liu, X., Ziska, K., Jeon, S., Yu, C.-L., & Xu, Y. (2024). Mathemyths: Leveraging large language models to teach mathematical language through Child-AI co-creative storytelling. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-23). ACM. https://doi.org/10.1145/3613904.3642647
Zhang, Y. L. (2025). Predicting teachers’ intentions for AIGC integration in preschool education: A hybrid SEM-ANN approach. Journal of Information Technology Education: Research, 24, Article 016. https://doi.org/10.28945/5502
Zhao, Y., Li, J., & Shu, S. (2024). DailyPhysics: Fostering physics concept exploration in children through a tangible AI storytelling approach. In Proceedings of the 2024 17^th International Symposium on Computational Intelligence and Design (pp. 231-234). IEEE. https://doi.org/10.1109/ISCID63852.2024.00059
Zhou, H., Nikolova, A., & An, P. (2024). ‘My lollipop dropped ...’–Probing design opportunities for SEL agents through children’s peer co-creation of social-emotional stories. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1-8). ACM. https://doi.org/10.1145/3613905.3651867

How to cite this article

APA

Zhang, Y., Halili, S. H. B., & Zainuddin, Z. (2026). Applications of generative AI in early childhood education: A systematic review. Eurasia Journal of Mathematics, Science and Technology Education, 22(3), em2793. https://doi.org/10.29333/ejmste/18068

Vancouver

Zhang Y, Halili SHB, Zainuddin Z. Applications of generative AI in early childhood education: A systematic review. EURASIA J Math Sci Tech Ed. 2026;22(3):em2793. https://doi.org/10.29333/ejmste/18068

AMA

Zhang Y, Halili SHB, Zainuddin Z. Applications of generative AI in early childhood education: A systematic review. EURASIA J Math Sci Tech Ed. 2026;22(3), em2793. https://doi.org/10.29333/ejmste/18068

Chicago

Zhang, Yuxin, Siti Hajar Binti Halili, and Zamzami Zainuddin. "Applications of generative AI in early childhood education: A systematic review". Eurasia Journal of Mathematics, Science and Technology Education 2026 22 no. 3 (2026): em2793. https://doi.org/10.29333/ejmste/18068

Harvard

Zhang, Y., Halili, S. H. B., and Zainuddin, Z. (2026). Applications of generative AI in early childhood education: A systematic review. Eurasia Journal of Mathematics, Science and Technology Education, 22(3), em2793. https://doi.org/10.29333/ejmste/18068

MLA

Zhang, Yuxin et al. "Applications of generative AI in early childhood education: A systematic review". Eurasia Journal of Mathematics, Science and Technology Education, vol. 22, no. 3, 2026, em2793. https://doi.org/10.29333/ejmste/18068