The Role of Artificial Intelligence in Enhancing Early Literacy in Early Childhood Education: A Systematic Literature Review

This Systematic Literature Review (SLR) investigates how Artificial Intelligence (AI) enhances early literacy learning and examines its pedagogical, ethical, and cultural implications within early childhood education. Guided by the PRISMA 2020 framework, 18 peer-reviewed studies published between 2020 and 2025 in Scopus and Web of Science databases were systematically reviewed. The synthesis highlights key AI applications, such as adaptive storytelling platforms, robot-assisted literacy tools, and generative text systems that are able to support vocabulary growth, reading comprehension, and active engagement among young learners. Findings indicate that AI fosters personalized, inclusive, and culturally responsive literacy instruction while transforming teachers into reflective designers, facilitators, and evaluators of learning. Nevertheless, issues such as limited AI literacy among educators, unequal access to digital resources, and ethical concerns surrounding privacy and algorithmic bias remain significant. The study concludes that sustainable AI integration requires continuous teacher training, robust ethical frameworks, and equitable technological access to advance inclusive and innovative early literacy education.

Artificial intelligence (AI); Early Childhood Education; Early literacy

1. Al-Bogami, R. M., & Alahmadi, N. A. (2025). Effects of an AI-Based Reading Progress Tool on Third-Grade EFL Learners’ Oral Reading Fluency. Computers and Education Open, 100283. https://doi.org/10.1016/j.caeo.2025.100283 [Google Scholar] [Crossref]

2. Arif, M., Ismail, A., & Irfan, S. (2025). AI-powered approaches for sustainable environmental education in the digital age: A study of Chongqing International Kindergarten. International Journal of Environment, Engineering and Education, 7(1), 35-47. https://doi.org/10.55151/ijeedu.v7i1.184 [Google Scholar] [Crossref]

3. Arn, L., & Huang, E. M. (2024). “Robots Can Do Disgusting Things, but Also Good Things”: Fostering Children’s Understanding of AI through Storytelling. ACM Transactions on Computing Education, 24(3), 1-55. https://doi.org/10.1145/3677613 [Google Scholar] [Crossref]

4. Azhar, M., Hin, H. S., & Kian, N. T. (2025, April). Enhancing Early Childhood Reading Skills Through Immersive AR and Conversational AI. In 2025 13th International Conference on Information and Education Technology (ICIET) (pp. 71-75). IEEE. https://doi.org/10.1109/ICIET66371.2025.11046302 [Google Scholar] [Crossref]

5. Bem-Haja, P., Nossa, P., Ferreira, A. J., Pereira, D. S., & Silva, C. F. (2025). AI analysis reveals top predictors of first grade success: Insights from multifactorial screening students’ early days of school. Computers and Education: Artificial Intelligence, 100415. https://doi.org/10.1016/j.caeai.2025.100415 [Google Scholar] [Crossref]

6. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

7. Daher, R. (2025). Integrating AI literacy into teacher education: a critical perspective paper. Discover Artificial Intelligence, 5(1), 217. https://doi.org/10.1007/s44163-025-00475-7 [Google Scholar] [Crossref]

8. Flemming, K., Booth, A., Garside, R., Tunçalp, Ö., & Noyes, J. (2019). Qualitative evidence synthesis for complex interventions and guideline development: Clarification of the purpose, designs and relevant methods. BMJ Global Health, 4(000882), 1–9. https://doi.org/10.1136/bmjgh-2018-000882 [Google Scholar] [Crossref]

9. Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181–217. https://doi.org/10.1002/jrsm.1378 [Google Scholar] [Crossref]

10. Kazanidis, I., & Pellas, N. (2024). Harnessing generative artificial intelligence for digital literacy innovation: A comparative study between early childhood education and computer science undergraduates. AI, 5(3), 1427-1445. https://doi.org/10.3390/ai5030068 [Google Scholar] [Crossref]

11. Kewalramani, S., Kidman, G., & Palaiologou, I. (2021). Using Artificial Intelligence (AI)-interfaced robotic toys in early childhood settings: a case for children’s inquiry literacy. European Early Childhood Education Research Journal, 29(5), 652-668. https://doi.org/10.1080/1350293X.2021.1968458 [Google Scholar] [Crossref]

12. Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26. [Google Scholar] [Crossref]

13. Kölemen, E. B., & Yıldırım, B. (2025). A new era in early childhood education (ECE): Teachers’ opinions on the application of artificial intelligence. Education and Information Technologies, 1-42. https://doi.org/10.1007/s10639-025-13478-9 [Google Scholar] [Crossref]

14. Lakhe Shrestha, B. L., Dahal, N., Hasan, M. K., Paudel, S., & Kapar, H. (2025). Generative AI on professional development: a narrative inquiry using TPACK framework. In Frontiers in Education (Vol. 10, p. 1550773). Frontiers Media SA. https://doi.org/10.3389/feduc.2025.1550773 [Google Scholar] [Crossref]

15. Li, X., & Yu, S. (2025). Culturally responsive AI module in early childhood teacher education: an action research. Journal of Early Childhood Teacher Education, 1-17. https://doi.org/10.1080/10901027.2025.2552973 [Google Scholar] [Crossref]

16. 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 (2071-1050), 16(24). https://doi.org/10.3390/su162411304 [Google Scholar] [Crossref]

17. 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 (2071-1050), 16(24). https://doi.org/10.3390/su162411304 [Google Scholar] [Crossref]

18. Luo, W., He, H., Gao, M., & Li, H. (2024). Safety, identity, attitude, cognition, and capability: The ‘SIACC’framework of early childhood AI literacy. Education Sciences, 14(8), 1-19. https://doi.org/10.3390/educsci14080871 [Google Scholar] [Crossref]

19. Messinger, D. S., Perry, L. K., Mitsven, S. G., Tao, Y., Moffitt, J., Fasano, R. M., ... & Jerry, C. M. (2022). Computational approaches to understanding interaction and development. In Advances in child development and behavior (Vol. 62, pp. 191-230). JAI. https://doi.org/10.1016/bs.acdb.2021.12.002 [Google Scholar] [Crossref]

20. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Bmj, 339. https://doi.org/10.1136/bmj.b2535 [Google Scholar] [Crossref]

21. Oyetade, K., & Zuva, T. (2025). Advancing Equitable Education with Inclusive AI to Mitigate Bias and Enhance Teacher Literacy. Educational Process: International Journal, 14, e2025087. https://doi.org/10.22521/edupij.2025.14.87 [Google Scholar] [Crossref]

22. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Medicine, https://doi.org/10.1371/journal.pmed.1003583 [Google Scholar] [Crossref]

23. Park, S. O., & Hassairi, N. (2021). What predicts legislative success of early care and education policies?: Applications of machine learning and Natural Language Processing in a cross-state early childhood policy analysis. Plos one, 16(2), e0246730. https://doi.org/10.1371/journal.pone.0246730 [Google Scholar] [Crossref]

24. Sanusi, I. T., Sunday, K., Oyelere, S. S., Suhonen, J., Vartiainen, H., & Tukiainen, M. (2024). Learning machine learning with young children: Exploring informal settings in an African context. Computer Science Education, 34(2), 161-192. https://doi.org/10.1080/08993408.2023.2175559 [Google Scholar] [Crossref]

25. Su, J., & Yang, W. (2024). Artificial intelligence and robotics for young children: Redeveloping the five big ideas framework. ECNU Review of Education, 7 (3), 685-698. https://us.sagepub.com/en-us/journals-permissions [Google Scholar] [Crossref]

26. Su, J., & Zhong, Y. (2022). Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Computers and Education: Artificial Intelligence, 3, 100072. https://doi.org/10.1016/j.caeai.2022.100072 [Google Scholar] [Crossref]

27. 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 (IJTEPD), 8(1), 1-23. https://doi.org/10.4018/IJTEPD.382379 [Google Scholar] [Crossref]

28. Wagner, C. J. (2024). Differentiating Children's Reading Materials with Artificial Intelligence: Exploring Possibilities for Personalized Learning. The Reading Teacher, 78(3), 191-194. https://doi.org/10.1002/trtr.2361 [Google Scholar] [Crossref]

29. Xu, Y., Aubele, J., Vigil, V., Bustamante, A. S., Kim, Y. S., & Warschauer, M. (2022). Dialogue with a conversational agent promotes children’s story comprehension via enhancing engagement. Child Development, 93(2), e149-e167. https://doi.org/10.1111/cdev.13708 [Google Scholar] [Crossref]

30. Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Comput. Educ. Artif. Intell., 3, 100061. https://doi.org/10.1016/j.caeai.2022.100061 [Google Scholar] [Crossref]

31. Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Comput. Educ. Artif. Intell., 3, 100061. https://doi.org/10.1016/j.caeai.2022.100061 [Google Scholar] [Crossref]

32. You, J., & Yang, X. (2024). Evaluation of the effectiveness of robot-assisted teaching in the training room in preschool education. Applied Mathematics and Nonlinear Sciences, 9. https://doi.org/10.2478/amns.2023.2.00338 [Google Scholar] [Crossref]

33. Zaidi, S. A. Z., Ahmad, E., & Shukla, N. (2024). Ethical Considerations in the Use of Artificial Intelligence (AI) for Education and Research: A Review. International Journal of Innovations in Science, Engineering And Management, 156-167. https://doi.org/10.69968/ijisem.2024v3si2156-167 [Google Scholar] [Crossref]

34. 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, 016. https://doi.org/10.28945/5502 [Google Scholar] [Crossref]

35. Zhao J. G. (2014). Combination of multiple databases is necessary for a valid systematic review. International orthopaedics, 38(12), 2639. https://doi.org/10.1007/s00264-014-2556-y [Google Scholar] [Crossref]

36. Zhu, J., & Liu, W. (2020). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8 [Google Scholar] [Crossref]

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