Üretken Yapay Zekânın 3–12 Yaş Çocukların Eğitiminde Pedagojik, Duyuşsal ve Kapsayıcı Etkileri: PRISMA Tabanlı Sistematik Bir İnceleme

Öz

Bu sistematik derleme, 3–12 yaş aralığındaki çocuklarda üretken yapay zekâ (ÜYZ) uygulamalarının akademik, duyuşsal ve kapsayıcılık çıktıları üzerindeki etkilerine ilişkin mevcut ampirik kanıtları PRISMA 2020 kılavuzu doğrultusunda sentezlemektedir. Altı veri tabanı ve gri literatür kaynaklarında yapılan kapsamlı tarama sonucunda, dahil edilme ölçütlerini karşılayan 42 çalışma incelenmiştir. Bulgular, ÜYZ’nin dil gelişimi, yazma becerileri, okuduğunu anlama ve matematik problem çözme gibi alanlarda öğrenmeyi desteklediğini, ayrıca motivasyon ve öz-düzenleme üzerinde olumlu etkiler yarattığını göstermektedir. Bununla birlikte veri güvenliği, etik yönetişim ve öğretmen arabuluculuğu, özellikle erken yaş gruplarında başarının kritik koşulları olarak öne çıkmaktadır. Çalışma, ÜYZ’nin pedagojik potansiyelini çocuk hakları, etik ilkeler ve kapsayıcı eğitim çerçevesinde yeniden tanımlamakta; gelecekteki araştırma, politika ve uygulamalara yönelik yol gösterici öneriler sunmaktadır.

Anahtar Kelimeler

• Üretken Yapay Zekâ
• Erken Çocukluk Eğitimi
• İlkokul
• Sistematik Derleme

Etik Beyan

Bu çalışma sistematik derleme yöntemine göre yapılmış ve yayımlanmış verilerin ikincil analizine dayandığı için ek etik kurul onayı gerektirmemektedir. Bununla birlikte, incelenen birincil çalışmaların etik onay beyanları, veri çıkarımı sırasında kayıt altına alınmıştır.

Pedagogical, Affective, and Inclusive Impacts of Generative Artificial Intelligence in the Education of Children Aged 3–12: A PRISMA–Based Systematic Review

Öz

This systematic review synthesizes the existing empirical evidence on the effects of generative artificial intelligence (GenAI) applications on academic, affective, and inclusive outcomes among children aged 3–12, in line with the PRISMA 2020 guidelines. A comprehensive search across six databases and grey literature sources identified 42 studies that met the inclusion criteria. The findings indicate that GenAI contributes to language development, writing skills, reading comprehension, and mathematical problem-solving, while also fostering motivation and self-regulation. At the same time, issues such as data security, ethical governance, and teacher mediation emerge as critical conditions for success, particularly in early childhood contexts. This review redefines the pedagogical potential of GenAI within the frameworks of children’s rights, ethical principles, and inclusive education, offering actionable recommendations for future research, policy, and practice.

Anahtar Kelimeler

• Generative Artificial Intelligence
• Early Childhood Education
• Primary Education
• Systematic Review

Etik Beyan

This study was conducted according to the systematic review method and is based on a secondary analysis of published data; therefore, it does not require additional ethics committee approval. However, the ethical approval statements of the primary studies reviewed were recorded during data extraction.

Kaynakça

1. Adeshola, I., & Adepoju, A. P. (2023). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments, 31(10), 7427-7440. https://doi.org/10.1080/10494820.2023.2253858
2. Akgün, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
3. Almahri, F. A. J., Bell, D., & Arzoky, M. (2022). The ethical challenges of AI in education: A systematic review. Computers and Education: Artificial Intelligence, 3, 100085. https://doi.org/10.1016/j.caeai.2022.100085
4. American Academy of Pediatrics. (2016). Media and young minds. Pediatrics, 138(5), e20162591. https://doi.org/10.1542/peds.2016-2591
5. Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1331877
6. Baz, M. A., Alhuwaydi, A. M., Alzahrani, S. S., & Alqarni, D. A. (2025). The effect of AI chatbot feedback on fifth-grade informative writing. Open Praxis, 17(3), 871-885. https://doi.org/10.55982/openpraxis.17.3.871
7. Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), eaat5954. https://doi.org/10.1126/scirobotics.aat5954
8. Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. International Journal of Artificial Intelligence in Education, 31(3), 527-541. https://doi.org/10.1007/s40593-020-00215-1

9. Bozkurt, A. (2024). Why generative AI literacy, why now? Open Praxis, 16(3), 267-283. https://doi.org/10.55982/openpraxis.16.3.739
10. Celik, I. (2023). Towards a human-centered AI in education: A critical review. Educational Technology Research and Development, 71(1), 1–24. https://doi.org/10.1007/s11423-022-10152-6
11. Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. Educational Technology Research and Development, 70(3), 2059-2087. https://doi.org/10.1007/s11423-022-10112-0
12. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
13. Chiu, T. K. F. (2023). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 5, 100157. https://doi.org/10.1016/j.caeai.2023.100157
14. Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
15. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 60(5), 681–689. https://doi.org/10.1080/14703297.2023.2190148
16. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
17. Çelik, İ. (2023). Exploring the determinants of artificial intelligence (AI) literacy: Digital divide, computational thinking, cognitive absorption. Telematics and Informatics, 83, 102026. https://doi.org/10.1016/j.tele.2023.102026
18. Demir, K., & Akpınar, E. (2023). The effect of artificial intelligence-based learning environments on student achievement and motivation: A meta-analysis study. Education and Information Technologies, 28(11), 14763-14788. https://doi.org/10.1007/s10639-023-11834-1
19. Deng, R., Wang, J., & Chen, L. (2024). Does ChatGPT enhance student learning? A systematic review and meta-analysis. Computers & Education, 207, 104896. https://doi.org/10.1016/j.compedu.2024.104896
20. Dignum, V. (2021). The role and challenges of education for responsible AI. London Review of Education, 19(1), 1-11. https://doi.org/10.14324/LRE.19.1.01
21. Etkin, H. K., Yilmaz, O., & Demir, E. (2025). Differential effects of GPT-based tools on reading comprehension by baseline ability in early elementary grades. Frontiers in Education, 10, Article 1506752. https://doi.org/10.3389/feduc.2025.1506752 Evmenova, A. S., Regan, K., Mergen, R., & Hrisseh, R. (2024). Improving writing feedback for struggling writers: Generative AI to the rescue. TechTrends, 68, 1190-1202. https://doi.org/10.1007/s11528-024-00965-y
22. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(2), 460-474. https://doi.org/10.1080/14703297.2023.2195846
23. Gharaibeh, M., Al-Smadi, M., Jarrah, M., & Alzyoud, S. (2025). The potential of ChatGPT in enhancing reading comprehension for children with dyslexia: A pilot intervention study. International Journal of Language & Communication Disorders. Advance online publication. https://doi.org/10.1111/1460-6984.13015
24. Goswami, U. (2014). Child cognitive development: The importance of the prefrontal cortex and executive functions. British Journal of Educational Psychology, 84(1), 1–23. https://doi.org/10.1111/bjep.12052
25. Heung, V. W. Y. (2025). The effects of generative AI on students’ emotional engagement: A meta-analysis. Computers and Education: Artificial Intelligence, 6, 100301. https://doi.org/10.1016/j.caeai.2025.100301
26. Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe. (Rapor) https://rm.coe.int/artificial-intelligence-and-education-a-critical-view-through-the-lens/1680a886bd
27. Hong, Q. N., Fàbregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., Gagnon, M.-P., Griffiths, F., Nicolau, B., O’Cathain, A., Rousseau, M.-C., Vedel, I., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285–291. https://doi.org/10.3233/EFI-180221
28. Huang, C., Li, Y., & Zhang, M. (2025). Enhancing student reading performance through GenAI-supported PBL. Humanities and Social Sciences Communications, 12, 299. https://doi.org/10.1057/s41599-025-04919-4
29. Jürgensmeier, L., Schneider, J., & Schiffner, D. (2024). Generative AI for scalable feedback on multimodal exercises in primary education. Computers & Graphics, 114, 1-13. https://doi.org/10.1016/j.cag.2024.03.005
30. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
31. 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 CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems Article 120 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642580
32. Liu, J., Wang, Q., & Zhang, H. (2025). Implementing ChatGPT-MPS for mathematics problem solving in primary classrooms: Effects on achievement and motivation. Computers & Education: Artificial Intelligence, 6, 100277. https://doi.org/10.1016/j.caeai.2025.100277
33. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed.pdf
34. NAEYC, & Fred Rogers Center. (2012). Technology and interactive media as tools in early childhood programs serving children from birth through age 8. National Association for the Education of Young Children. https://www.naeyc.org/sites/default/files/globally-shared/downloads/PDFs/resources/position-statements/ps_technology.pdf
35. Organisation for Economic Co-operation and Development. (2023). Education at a glance 2023: OECD indicators. OECD Publishing. https://doi.org/10.1787/69096873-en
36. Organization for Economic Co-operation and Development [OECD]. (2019). OECD AI principles. OECD Publishing. https://oecd.ai/en/ai-principles
37. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J., 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., McGuinness, L. A., … Welch, V. A. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 89. https://doi.org/10.1186/s13643-021-01626-4
38. 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. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
39. Piaget, J. (1952). The origins of intelligence in children (M. Cook, Trans.). International Universities Press. (Original work published 1936)
40. Saborío-Taylor, S., & Rojas-Ramírez, F. (2024). Universal design for learning and AI in the digital era. International Journal of Professional Development, Learners and Learning, 6(2), ep2408. https://doi.org/10.30935/ijpdll/14694
41. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
42. Song, Y., Wang, J., & Chen, X. (2024). A framework for inclusive AI learning design for diverse learners in primary education. Computers & Education: Artificial Intelligence, 5, 100201. https://doi.org/10.1016/j.caeai.2024.100201
43. Steiss, J., Tate, T., Graham, S., Cruz, J., Hebert, M., Wang, J., Moon, Y., Tseng, W., & Warschauer, M. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction, 91, 101894. https://doi.org/10.1016/j.learninstruc.2024.101894
44. Sterne, J. A. C., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., … Higgins, J. P. T. (2016). ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 355, i4919. https://doi.org/10.1136/bmj.i4919
45. Su, J., & Yang, W. (2022). A systematic review of integrating AI into early childhood education: Implications for research and practice. Computers and Education Open, 3, 100076. https://doi.org/10.1016/j.caeo.2022.100076
46. Tai, T.-Y., & Chen, J. C. (2024). Improving elementary EFL learners’ speaking skills through interactions with generative AI chatbots: A randomized controlled trial. Computers & Education, 205, 105112. https://doi.org/10.1016/j.compedu.2024.105112
47. TeachAI. (2024). AI guidance for schools toolkit. https://www.teachai.org/toolkit
48. Timms, M. J. (2016). Letting artificial intelligence in education out of the box: Educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712. https://doi.org/10.1007/s40593-016-0095-y
49. U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www2.ed.gov/documents/ai-report/ai-report.pdf
50. UN Committee on the Rights of the Child. (2021). General Comment No. 25 (2021) on children’s rights in relation to the digital environment. CRC/C/GC/25. https://www.ohchr.org/en/documents/general-comments-and-recommendations/general-comment-no-25-2021-children’s-rights-relation
51. UNICEF. (2021). Policy guidance on AI for children (2nd ed.). UNICEF. https://www.unicef.org/globalinsight/media/2356/file/UNICEF-Global-Insight-policy-guidance-AI-children-2.0-2021.pdf
52. United Nations Educational, Scientific and Cultural Organization [UNESCO]. (2011). International Standard Classification of Education: ISCED 2011. UNESCO Institute for Statistics. https://uis.unesco.org/en/topic/international-standard-classification-education-isced
53. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
54. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025
55. Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025