AGE STAGES IN THE FORMATION OF PROMPTING ABILITY IN CHILDREN
DOI:
https://doi.org/10.47390/ydif-y2026v2i12/n29Keywords:
Artificial intelligence (AI), large language models (LLM), prompting skills, cognitive development, J. Piaget, L.S. Vygotsky, Bloom's Taxonomy, 5W1H template, Chain-of-Thought, meta-prompting, pedagogical model, digital literacy.Abstract
In this article, one of the urgent tasks of the education system in the era of rapid development of large language models (LLM) and artificial intelligence (AI) technologies - the age stages of the formation of the ability to "prompt" (form a clear and targeted request to AI systems) in children are scientifically studied. The theoretical foundation of the study is J. Piaget's theory of operational intelligence, L.S. Vygotsky's cultural-historical psychology and B. Bloom's taxonomy. The article, based on the characteristics of children's cognitive development, classifies prompting education into four main age stages: preschool age (4–6 years) — verbal expression, primary school (7–10 years) — structured inquiry based on the "5W1H" template, high school (11–13 years) — strategic prompting based on the Chain-of-Thought method, and adolescence (14–16 years) — independent professional and meta-prompting stage. A comparative analysis of SI tools, methodological approaches, and their empirical effectiveness indicators suitable for each period is conducted. Finally, a four-stage pedagogical model for integrating this system into the national curricula of Uzbekistan is proposed and limitations such as regional differences in digital infrastructure are discussed.
References
1. OpenAI. (2024). GPT-4 Technical Report. OpenAI Research. https://openai.com/research/gpt-4
2. Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3560815
3. Piaget, J. (1952). The Origins of Intelligence in Children. International Universities Press. (O‘zbek tiliga tarjima: Piaje J. Boladagi aql-idrokning kelib chiqishi. Toshkent: Fan, 1998.)
4. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
5. Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. David McKay.
6. White, J., & Leroy, G. (2023). Prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv preprint arXiv:2302.11382 .
7. Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny can't prompt: How non-AI experts try (and fail) to design LLM prompts. In Proceedings of CHI 2023 (pp. 1–21). ACM. https://doi.org/10.1145/3544548.3581388
8. Zhao, W., & Liu, H. (2024). Developmental trajectories of prompt literacy in K-12 education: A meta-analysis. Computers & Education, 198, 104789. https://doi.org/10.1016/j.compedu.2023.104789
9. Morrison, A., Baker, R., & Nakamura, T. (2022). Voice-based AI interaction in early childhood settings: Linguistic outcomes and pedagogical implications. Early Childhood Education Journal, 50(4), 623–641. https://doi.org/10.1007/s10643-021-01224-6
10. Kim, S., & Park, J. (2023). Structured prompt templates for elementary school AI literacy: A quasi-experimental study. Journal of Educational Technology & Society, 26(2), 88–104.
11. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems 35. NeurIPS.
12. Rashid, M., & Hernandez, C. (2024). Chain-of-thought prompting in middle school mathematics: A randomized controlled trial. Educational Psychology Review, 36(1), 1–24. https://doi.org/10.1007/s10648-023-09815-6
13. Anderson, L., Davies, P., & Osei, K. (2024). Meta-prompting competencies in adolescent learners: Assessment frameworks and instructional strategies. British Journal of Educational Technology, 55(3), 1102–1118. https://doi.org/10.1111/bjet.13412
14. Watanabe, H., & Gupta, A. (2023). AI literacy and future workforce competencies: A Delphi study. Journal of Vocational Education & Training, 75(2), 289–307. https://doi.org/10.1080/13636820.2022.2144811
15. Kasimova, M. (2025). Sun'iy intellekt yordamida xorijiy tillarni o'rganishda samarali promptlardan foydalanish. Yevroosiyo ijtimoiy fanlar, falsafa va madaniyat jurnali , 5 (1), 13–15.
This work is licensed under a 