Schools in the post-ChatGPT era: A systematic review of human-AI interaction, learning, and agency in science education
DOI:
https://doi.org/10.22600/1518-8795.ienci/2026v31n3p260Keywords:
generative artificial intelligence, science education, learning, human agency, systematic reviewAbstract
The launch of ChatGPT in late 2022 has ushered in a new era of uncertainties and possibilities in education. This article presents a systematic review of how students and teachers interact with Generative Artificial Intelligence (GAI) tools in K-12 science education, following the PRISMA guidelines. We analyzed 17 studies published since 2023. The results show that the quality of these interactions depends on the user's prior capabilities (especially disciplinary knowledge), which are prerequisites for maintaining agency. When users preserve their agency, outcomes tend to be better than when agency is delegated to the tool. The same reduction in effort that can free students to think more deeply can, under other conditions, simply eliminate the need for them to think at all. We highlight some risks: superficial thinking, uncritical dependence, and the exacerbation of educational inequalities. The findings have implications for educational practice, emphasizing the importance of the pedagogical and intentional use of GAI for learning, aimed at preserving student and teacher agency. Regarding research in this area, we identified some gaps within the scope of this review: the absence of studies on medium- and long-term effects on learning, and the scarcity of investigations into potentially harmful effects.References
Aptyka, H., Großschedl, J., & Hartelt, T. (2025). Bugbear or surefire success? Secondary school students’ conceptual learning about evolution with ChatGPT. International Journal of Science Education. https://dx.doi.org/10.1080/09500693.2025.2524083
Bandura, A. (2018). Toward a psychology of human agency: Pathways and reflections. Perspectives on Psychological Science, 13(2), 130–136. https://dx.doi.org/10.1177/1745691617699280
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://dx.doi.org/10.1073/pnas.2422633122
Beck, U. (2010). Sociedade de risco: Rumo a uma outra modernidade. Editora 34.
Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://dx.doi.org/10.30935/cedtech/13176
Chaiklin, S. (2011). A zona de desenvolvimento próximo na análise de Vigotski sobre aprendizagem e ensino (J. C. Pasqualini, Trad.). Psicologia em Estudo, 16(4), 659–675. https://dx.doi.org/10.1590/S1413-73722011000400016
Chen, C.-H., & Chang, C.-L. (2024). Effectiveness of AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior. Education and Information Technologies, 29(14), 18621–18642. https://dx.doi.org/10.1007/s10639-024-12553-x
Chen, C.-H., & Law, V. (2025). The role of help-seeking from ChatGPT in digital game-based learning. Educational Technology Research and Development, 73(3), 1703–1721. https://dx.doi.org/10.1007/s11423-025-10464-3
Cheung, K. K. C., Long, Y., Liu, Q., & Chan, H.-Y. (2025). Unpacking epistemic insights of artificial intelligence (AI) in science education: A systematic review. Science & Education, 34(2), 747–777. https://dx.doi.org/10.1007/s11191-024-00511-5
Cho, N., Li, H., Zhang, X., & Li, Y. (2025). Physics teachers’ insights into the usability and challenges of a platform with integrated simulations and generative AI. Journal of Science Education and Technology. https://dx.doi.org/10.1007/s10956-025-10280-7
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., & Calvert, M. J. (2020). Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nature Medicine, 26(9), 1351–1363. https://dx.doi.org/10.1038/s41591-020-1037-7
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://dx.doi.org/10.1016/j.compedu.2024.105224
Dizon, J., & Prudente, M. S. (2025). AI-powered chatbots in science education: Meta-analysis of pedagogical integration and student achievement. International Journal of Chemistry Education Research, 9(2), 134–150. https://dx.doi.org/10.20885/ijcer.vol9.iss2.art4
Fesler, L., Claeys, J. M., Agnew, C., & Loeb, S. (2026). The evidence base on AI in K-12: A 2026 review. Stanford University. https://scale.stanford.edu/sites/default/files/The%20Evidence%20Base%20on%20AI%20in%20K-12%20Report.pdf
Fock, A., & Siller, H.-S. (2025). Generative artificial intelligence in secondary STEM education in the light of Human Flourishing: A scoping literature review. International Journal of STEM Education, 12(1), 67. https://dx.doi.org/10.1186/s40594-025-00589-5
Georgiou, G. P. (2025). ChatGPT produces more “lazy” thinkers: Evidence of cognitive engagement decline (arXiv:2507.00181). arXiv. https://dx.doi.org/10.48550/arXiv.2507.00181
Gunbatar, S. A., & Sirin, G. T. (2025). Does using prompting strategies affect the production of higher-quality socio-scientific issue activity? Analysis of nuclear energy and pesticide use cases for high school chemistry teaching. Journal of Chemical Education, 102(11), 4676–4688. https://dx.doi.org/10.1021/acs.jchemed.5c00684
Gunbatar, S. A., Sirin, G. T., Ilkyaz, O. C., & Mutlu, Y. (2025). Exploring the artificial intelligence interaction profiles of participants with different levels of teaching experience for lesson planning in the context of acids and bases. Chemistry Education Research and Practice, 26(4), 977–995. https://dx.doi.org/10.1039/d5rp00064e
Gunes, L., & Yilmazlar, M. (2025). The effect of REACT strategy integrated with artificial intelligence on academic achievement. International Journal of Education in Mathematics, Science and Technology, 13(5), 1083–1096. https://dx.doi.org/10.46328/ijemst.5698
Guo, Q., Zhen, J., Wu, F., He, Y., & Qiao, C. (2025). Can students make STEM progress with the large language models (LLMs)? An empirical study of LLMs integration within middle school science and engineering practice. Journal of Educational Computing Research, 63(2), 372–405. https://dx.doi.org/10.1177/07356331241312365
Hosseini, M.-S., Jahanshahlou, F., Akbarzadeh, M. A., Zarei, M., & Vaez-Gharamaleki, Y. (2024). Formulating research questions for evidence-based studies. Journal of Medicine, Surgery, and Public Health, 2, 100046. https://dx.doi.org/10.1016/j.glmedi.2023.100046
Huang, L., Zhou, X., & Zhu, W. (2026). The teaching model (IT-CSTP) for integrating intelligent technology into scientific thinking cultivation in physics: A practical study for high school students. Journal of Science Education and Technology, 35(1), 27–44. https://dx.doi.org/10.1007/s10956-025-10225-0
Kilde-Westberg, S., Johansson, A., & Enger, J. (2025). Generative AI as a lab partner: A case study. Physical Review Physics Education Research, 21(2), 020119. https://dx.doi.org/10.1103/ggy1-3kjk
Krakowski, S. (2025). Human-AI agency in the age of generative AI. Information and Organization, 35(1), 100560. https://dx.doi.org/10.1016/j.infoandorg.2025.100560
Kreijkes, P., Kewenig, V., Kuvalja, M., Lee, M., Hofman, J. M., Vitello, S., Sellen, A., Rintel, S., Goldstein, D. G., Rothschild, D., Tankelevitch, L., & Oates, T. (2026). Effects of LLM use and note-taking on reading comprehension and memory: A randomised experiment in secondary schools. Computers & Education, 243, 105514. https://dx.doi.org/10.1016/j.compedu.2025.105514
Krupp, L., Steinert, S., Kiefer-Emmanouilidis, M., Avila, K. E., Lukowicz, P., Kuhn, J., Küchemann, S., & Karolus, J. (2024). Unreflected acceptance – Investigating the negative consequences of ChatGPT-assisted problem solving in physics education. Em HHAI 2024: Hybrid human AI systems for the social good (Frontiers in Artificial Intelligence and Applications, Vol. 386, pp. 199–212). IOS Press. https://dx.doi.org/10.3233/FAIA240195
Kundu, A., & Bej, T. (2025). Psychological impacts of AI use on school students: A systematic scoping review of the empirical literature. Research and Practice in Technology Enhanced Learning, 20, 30. https://dx.doi.org/10.58459/rptel.2025.20030
Lai, C. L. (2025). Consolidate knowledge or build scientific models? The role of online information-searching strategies in students’ prompt sequences with generative artificial intelligence. Educational Technology & Society, 28(3), 241–262. https://dx.doi.org/10.30191/ETS.202507_28(3).TP03
Latif, E., & Zhai, X. (2024). Fine-tuning ChatGPT for automatic scoring. Computers and Education: Artificial Intelligence, 6, 100210. https://dx.doi.org/10.1016/j.caeai.2024.100210
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
Lee, C., & So, H.-J. (2025). Learners leveraging generative AI for creative problem solving: Focusing on the PISA 2022 creative thinking problems. Educational Technology & Society, 28(4), 241–258. https://dx.doi.org/10.30191/ETS.202510_28(4).SP04
Lintner, T., Juhaňák, L., Žáková, M., Plch, L., & Ropovik, I. (2026). Generative AI use and academic achievement: A cross-sectional study of Czech seventh-grade students. Education and Information Technologies. https://dx.doi.org/10.1007/s10639-026-14002-3
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., & Denniston, A. K. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nature Medicine, 26(9), 1364–1374. https://dx.doi.org/10.1038/s41591-020-1034-x
Meyer, A., Bleckmann, T., & Friege, G. (2025). Automatic feedback on physics tasks using open-source generative artificial intelligence. International Journal of Science Education. https://dx.doi.org/10.1080/09500693.2025.2499220
Monteiro, F. F., Souza, P. V. S., Silva, M. C. da, Maia, J. R., Silva, W. F. da, & Girardi, D. (2024). ChatGPT in Brazilian K-12 science education. Frontiers in Education, 9, 1321547. https://dx.doi.org/10.3389/feduc.2024.1321547
Narayan, N. (2025). AI, cognition, and the cost of convenience. Journal of Technology and Systems, 7(6), 18–34. https://dx.doi.org/10.47941/jts.3185
Naumann, L., Mayr, J. L., & Markic, S. (2025). Between acceptance and scepticism: An investigation into secondary school students’ attitudes toward artificial intelligence in chemistry education. CHIMIA, 79(10), 693–697. https://dx.doi.org/10.2533/chimia.2025.693
Nyaaba, M., Zhai, X., & Faison, M. Z. (2024). Generative AI for culturally responsive science assessment: A conceptual framework. Education Sciences, 14(12), 1325. https://dx.doi.org/10.3390/educsci14121325
Otto, S., Lavi, R., & Bertel, L. B. (2025). Human-GenAI interaction for active learning in STEM education: State-of-the-art and future directions. Computers & Education, 239, 105444. https://dx.doi.org/10.1016/j.compedu.2025.105444
Page, M. J., Moher, D., 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., . . . McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ, 372, n160. https://dx.doi.org/10.1136/bmj.n160
Potter, A. H., Arner, T., McCarthy, K. S., & McNamara, D. S. (2026). Integrating reading, writing, and digital tools in science: A participatory-design study of the InSPECT framework. Education Sciences, 16(1), 6. https://dx.doi.org/10.3390/educsci16010006
Prestes, Z. R. (2010). Quando não é quase a mesma coisa: Análise de traduções de Lev Semionovitch Vigotski no Brasil – repercussões no campo educacional [Tese (Doutorado), Universidade de Brasília]. https://repositorio.unb.br/handle/10482/9123
Raitskaya, L., & Tikhonova, E. (2025). Enhancing critical thinking skills in ChatGPT-human interaction: A scoping review. Journal of Language and Education, 11(2), 5–19. https://dx.doi.org/10.17323/jle.2025.27387
Rivera-Novoa, A., & Duarte Arias, D. A. (2025). Generative artificial intelligence and extended cognition in science learning contexts. Science & Education. https://dx.doi.org/10.1007/s11191-025-00660-1
Sasikala, P., & Ravichandran, R. (2024). Study on the impact of artificial intelligence on student learning outcomes. Journal of Digital Learning and Education, 4(2), 145–155. https://dx.doi.org/10.52562/jdle.v4i2.1234
Shen, J. H., & Tamkin, A. (2026). How AI impacts skill formation (arXiv:2601.20245). arXiv. https://dx.doi.org/10.48550/arXiv.2601.20245
Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386. https://dx.doi.org/10.1016/j.chb.2024.108386
Sweller, J. (2011). Cognitive load theory. Em J. P. Mestre & B. H. Ross (Orgs.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. https://dx.doi.org/10.1016/B978-0-12-387691-1.00002-8
Tang, K.-S., & Cooper, G. (2025). The role of materiality in an era of generative artificial intelligence. Science & Education, 34(2), 731–746. https://dx.doi.org/10.1007/s11191-024-00508-0
Tong, D., Jin, B., Tao, Y., Ren, H., Islam, A. Y. M. A., & Bao, L. (2025). Exploring the role of human-AI collaboration in solving scientific problems. Physical Review Physics Education Research, 21(1), 010149. https://dx.doi.org/10.1103/PhysRevPhysEducRes.21.010149
Valeri, F., Nilsson, P., & Cederqvist, A.-M. (2025). Exploring students’ experience of ChatGPT in STEM education. Computers and Education: Artificial Intelligence, 8, 100360. https://dx.doi.org/10.1016/j.caeai.2024.100360
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Vygotsky, L. S. (1991). A formação social da mente (4ª ed.). Martins Fontes.
Wang, S., Chen, F., Ge, C., Shi, H., You, Y., Guo, J., Zheng, S., & Zhu, X. (2025). Using local LLM tools to optimize Chinese high school chemistry education: Practice, challenges, and future directions. Journal of Chemical Education, 102(10), 4368–4375. https://dx.doi.org/10.1021/acs.jchemed.5c00220
Wenker, K. (2023). Who wrote this? How smart replies impact language and agency in the workplace. Telematics and Informatics Reports, 10, 100062. https://dx.doi.org/10.1016/j.teler.2023.100062
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://dx.doi.org/10.1111/j.1469-7610.1976.tb00381.x
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), 28. https://dx.doi.org/10.1186/s40561-024-00316-7
Zhang, J., Jantakoon, T., & Laoha, R. (2025). Meta-analysis of artificial intelligence in education. Higher Education Studies, 15(2), 189. https://dx.doi.org/10.5539/hes.v15n2p189
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rodrigo Travitzki
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IENCI is an Open Access journal, which does not have to pay any charges either for the submission or processing of articles. The journal has adopted the definition of the Budapest Open Access Initiative (BOAI), which states that the users have the right to read, write down, copy, distribute, print, conduct searches and make direct links with the complete texts of the published articles.
The author responsible for the submission represents all the authors of the work and when the article is sent to the journal, guarantees that he has the permission of his/her co-authors to do so. In the same way, he/she provides an assurance that the article does not infringe authors´ rights and that there are no signs of plagiarism in the work. The journal is not responsible for any opinions that are expressed.
All the articles are published with a Creative Commons License Attribution Non-commercial 4.0 International. The authors hold the copyright of their works and must be contacted directly if there is any commercial interest in the use of their works.
