Generative AI and education: dynamic personalization of pupils’ school learning material with ChatGPT
Open accessAbstract
The widespread use of generative AI tools like ChatGPT has seen significant growth. This rise prompted discussions on integrating these technologies into school education. However, the practical implementation, testing, and assessment of generative AI in primary and secondary education remained largely unexplored. This article examines the application of ChatGPT-3.5 and 4 in primary school education. A study involving 110 students aged 8–14 across grades 4–6 in two Uruguayan schools was conducted. The focus was on using generative AI for dynamic personalization of educational content during classroom lessons. In these sessions, instructional content followed the curriculum goals, and text, illustrations, and exercises were generated and dynamically adjusted based on generative AI. The findings indicate that generative AI effectively tailors school materials to match varying pupil knowledge levels. Real-time adjustments during lessons cater to individual learning needs, enhancing cognitive ergonomics. This approach not only boosts pupil motivation but also improves their performance, facilitating more effective achievement of the curriculum’s learning objectives. These results suggest a promising avenue for leveraging generative AI to personalize and optimize primary school education.
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Some studies integrate social-emotional and interdisciplinary tasks, where AI provides focused evaluations and follow-up recommendations that help students refine learning strategies and strengthen self-regulation.
“Some studies also integrate social-emotional and interdisciplinary tasks, where AI provides focused evaluations and follow-up recommendations that help students refine learning strategies and strengthen self-regulation [121,129,151].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [151]
Section 4.2 (page unavailable) describes prompt assessment of pupils’ answers and reassignment to learning materials suited to their performance; it also suggests that personalized materials may support self-regulation. But Section 3.2 describes a social-sciences history lesson, not social-emotional or interdisciplinary tasks, and the paper does not report AI giving pupils focused evaluations or follow-up recommendations to refine their learning strategies. (LLM, conf=0.85)
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Válek, J., & Sládek, P. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3155/1/012005
"Prompting" en ELE: formación docente y uso didáctico de la IA generativa
Manzanedo, M. S. (2025) · Doblele Revista de lengua y literatura · doi:10.5565/rev/doblele.171
Teacher-Assisted Learning Recommendation Platform for School Students
Anthony Pavithra, A., & Padmapriya, V. (2025) · International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS) · doi:10.1109/csitss67709.2025.11294888
Current Trends in AI-Supported Instruction in English Language Teaching
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Uzun, Y. (2025) · Necmettin Erbakan Universitesi Eregli Egitim Fakultesi Necmettin Erbakan University · doi:10.51119/ereegf.2025.139
Personalized Learning System for Employee Training Using Generative AI Technology
Fatt, Y. K., Sian, T. B., Chi, P. G., Keat, L. C., & Soon, C. K. (2025) · International Conference on Digital Transformation and Applications (ICDXA) · doi:10.1109/icdxa69105.2025.11330115
The games and education iceberg: Going beyond the surface to deeper learning
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GAGA: Game Asset Generative AI Pipeline
Li, T., Tim, T., Sun, R., Ng, Q. T., & Bhojan, A. (2025) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale66047.2025.11346631
Sumarta, Kamil, H. A. B., & Miftakh, F. (2026) · Jurnal Pengabdian Masyarakat dan Riset Pendidikan · doi:10.31004/jerkin.v4i3.5206
Personalizing Assessments with Generative AI: A Case Study from Moroccan Teacher Training at Um6p
Mimoudi, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-01638-6_15
Understanding Pre-Service Teacher Dialogues with ChatGPT for Learning Support
Humairoh, A. N. A., & Ubaidah, U. (2025) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss66954.2025.11389374
Bridging Froebel and AI: Reconceptualizing Play Pedagogy in Chinese Context
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- Authors
- Jauhiainen, J. S., & Garagorry Guerra, A.
- Affiliations
- University of Turku (FI)
- Corresponding author
- Jauhiainen, Jussi S.
- ORCID
- Jauhiainen, Jussi S.
- Venue
- Frontiers in Education
- Publisher
- Frontiers Media SA
- Published
- 2024-11-19
- Volume
- 9
- ISSN
- 2504-284X (ISSN-L)
- OpenAlex
- W4404509460
- Cited by
- 87
- References
- 46
- Citations by year
- 2026: 54 · 2025: 32 · 2024: 1
- FWCI
- 32.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Education (publishedVersion, cc-by)
- UTUPub (University of Turku) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Turun Yliopisto
- Business Finland
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI in education, ChatGPT, personalized learning, primary education, ChatGPT-4, student motivation, student performance, cognitive ergonomics, curriculum-based instruction, AI-generated educational content
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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