Generative Artificial Intelligence in High School Science Education: A Systematic Review of Learning Outcomes, Pedagogical Strategies, and Critical Thinking Development
Article Sidebar
Main Article Content
Abstract
Background and Aim: Evidence on generative artificial intelligence (AI) in secondary science education remains limited. This systematic review examined students’ conceptual understanding and engagement, teachers’ pedagogical strategies and challenges, and students’ critical thinking and scientific inquiry.
Materials and Methods: Following PRISMA 2020, ERIC, Scopus, and Lens.org were searched for peer-reviewed empirical studies published between 2022 and 2026. Two authors independently screened 187 unique records (Cohen’s κ = .78), identifying 20 eligible studies. Methodological quality was assessed using the Mixed Methods Appraisal Tool. Framework synthesis integrated findings, informed by Technological Pedagogical Content Knowledge (TPACK).
Results: Seven studies assessed AI capabilities rather than classroom implementation. Only two compared learning outcomes across conditions; none employed a randomized controlled design. Student perceptions and engagement were generally positive, whereas learning outcomes were mixed. A chatbot supporting self-regulated learning outperformed a rule-based chatbot, but ChatGPT-4o yielded smaller gains than peer collaboration. Teachers reported concerns regarding content accuracy, assessment reliability, and instructional readiness. Generative AI matched or exceeded student performance on structured examinations but demonstrated lower grading reliability than human assessors. Evidence concerning critical thinking was particularly limited. Richer reasoning was reported with dialogue-oriented tools or teacher guidance, whereas answer-seeking use of general-purpose tools was associated with weaker reasoning.
Conclusion: Pedagogical structuring was recurrently associated with productive AI use, although causal inference remains limited. A provisional model warrants empirical testing. Priorities include structured tasks, science-specific teacher development, human assessment oversight, explicit AI literacy instruction, school governance, and controlled comparisons of structured and unstructured use across secondary science classrooms.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright and License
Copyright of all articles published in the Journal of Education and Learning Reviews (JELR) is retained by the author(s).
All articles are distributed under the Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Users may read, download, copy, distribute, and share the content for non-commercial purposes with proper attribution.
Modification, adaptation, or commercial use of the content is not permitted.
License details: https://creativecommons.org/licenses/by-nc-nd/4.0/
References
Adeyele, V. O., & Ramnarain, U. (2024). Exploring the integration of ChatGPT in inquiry-based learning: Teacher perspectives. International Journal of Technology in Education, 7(2), 200–217. https://doi.org/10.46328/ijte.638
Ajose, S. T., Apata, O. E., Saidu, G. O., & Orobator, E. S. (2026). Exploring teachers’ awareness and perceptions of ChatGPT in K–12 STEM education. International Journal of Technology in Education, 9(1), 260–278. https://doi.org/10.46328/ijte.5077
Azizi, M. N., Faizi, A., & Sari, U. (2026). Artificial intelligence in physics education (2015–2025): Systematic review of trends, applications, and challenges. Journal of Education in Science, Environment and Health, 12(2), 174–197. https://doi.org/10.55549/jeseh.905
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), Article e2422633122. https://doi.org/10.1073/pnas.2422633122
Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), Article ep430. https://doi.org/10.30935/cedtech/13176
Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., Welch, V., & Thomson, H. (2020). Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. BMJ, 368, Article l6890. https://doi.org/10.1136/bmj.l6890
Cheung, K. K. C., Zerouali, A., Koenen, J., & Erduran, S. (2025). Do generative artificial intelligence (GenAI) and science education mix? A systematic review of the literature. Studies in Science Education. Advance online publication. https://doi.org/10.1080/03057267.2025.2578091
Co, S. J., Dabu, J. P., & Sanchez, P. J. (2026). AI literacy and academic performance: A cross-sectional analysis of senior high school students. International Journal of Technology in Education, 9(2), 476–490. https://doi.org/10.46328/ijte.5335
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, Article 105224. https://doi.org/10.1016/j.compedu.2024.105224
Fadillah, M. A., Usmeldi, & Asrizal. (2024). The role of ChatGPT and higher-order thinking skills as predictors of physics inquiry. Journal of Baltic Science Education, 23(6), 1178–1192. https://doi.org/10.33225/jbse/24.23.1178
Fekets, G. (2025). Development of an artificial intelligence-supported chatbot as an interactive learning platform in STEM education: Exploring usability and student experience. Journal of Baltic Science Education, 24(5), 878–893. https://doi.org/10.33225/jbse/25.24.878
Fernández, A. A., López-Torres, M., Fernández, J. J., & Vázquez-García, D. (2024). ChatGPT as an instructor’s assistant for generating and scoring exams. Journal of Chemical Education, 101(9), 3780–3788. https://doi.org/10.1021/acs.jchemed.4c00231
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, Article 67. https://doi.org/10.1186/s40594-025-00589-5
Fütterer, T., Bardach, L., Kuhn, J., Keller, S. D., & Gerjets, P. (2026). Enhancing school students’ self-regulated learning through generative AI support: A randomized controlled trial. Educational Psychology Review, 38(1), Article 42. https://doi.org/10.1007/s10648-026-10133-8
Garcia, R., Fojas, J. G., Formanes, J. N., Garcia, D., Habol, C., & Orapa, L. (2025). Perspective of science educators and students on the use of ChatGPT: A study of artificial intelligence. International Journal of Technology in Education and Science, 9(4), 474–491. https://doi.org/10.46328/ijtes.649
Gunsaldi, M. S., Guner, E. G., Uckan, M., & Bati, K. (2025). The impact of generative AI applications on student learning outcomes in science education: A systematic review. Journal of Education in Science, Environment and Health, 11(3), 196–208. https://doi.org/10.55549/jeseh.840
Hong, Q. N., Pluye, P., 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. (2018). Mixed Methods Appraisal Tool (MMAT), version 2018: User guide. McGill University. https://mixedmethodsappraisaltoolpublic.pbworks.com/w/file/127916259/MMAT_2018_criteria-manual_2018-08-01_ENG.pdf
Jang, H., & Choi, H. (2025). A double-edged sword: Physics educators’ perspectives on utilizing ChatGPT and its future in classrooms. Journal of Science Education and Technology, 34(2), 267–283. https://doi.org/10.1007/s10956-024-10173-1
Jere, S. (2025). Evaluating artificial intelligence large language models’ performances in a South African high school chemistry exam. Eurasia Journal of Mathematics, Science and Technology Education, 21(2), Article em2582. https://doi.org/10.29333/ejmste/15932
Jho, H., & Ha, M. (2024). Towards effective argumentation: Design and implementation of a generative AI-based evaluation and feedback system. Journal of Baltic Science Education, 23(2), 280–291. https://doi.org/10.33225/jbse/24.23.280
Kilde-Westberg, S., Johansson, A., & Enger, J. (2025). Generative AI as a lab partner: A case study. Physical Review Physics Education Research, 21(2), Article 020119. https://doi.org/10.1103/ggy1-3kjk
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
Lee, H.-Y., Huang, Y.-M., & Wu, T.-T. (2025). ChatGPT in education: A systematic review of current landscape, limitations and future directions through general system theory lens. European Journal of Education, 60(4), Article e70262. https://doi.org/10.1111/ejed.70262
Marzano, D. (2026). Generative artificial intelligence (GAI) in teaching and learning processes at the K–12 level: A systematic review. Technology, Knowledge and Learning, 31, 789–829. https://doi.org/10.1007/s10758-025-09853-7
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/EWZM9535
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
Ng, D. T. K., Tan, C. W., & Leung, J. K. L. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55(4), 1328–1353. https://doi.org/10.1111/bjet.13454
Nguyen, H., Nguyen, V., Ludovise, S., & Santagata, R. (2025). Value-sensitive design of chatbots in environmental education: Supporting identity, connectedness, well-being and sustainability. British Journal of Educational Technology, 56(4), 1370–1390. https://doi.org/10.1111/bjet.13568
Osborne, J. (2014). Teaching scientific practices: Meeting the challenge of change. Journal of Science Teacher Education, 25(2), 177–196. https://doi.org/10.1007/s10972-014-9384-1
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016). Rayyan—A web and mobile app for systematic reviews. Systematic Reviews, 5, Article 210. https://doi.org/10.1186/s13643-016-0384-4
Page, M. J., McKenzie, J. E., 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., . . . Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Pérez-Montesdeoca, H., Rodriguez-Rodriguez, D., & Fernández-Sogorb, A. (2026). The integration of generative artificial intelligence in secondary education: A systematic review. Contemporary Educational Technology, 18(3), Article ep667. https://doi.org/10.30935/cedtech/18746
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), Article 6. https://doi.org/10.3390/educsci16010006
Pun, J., Cheung, K. K. C., & Kenneth-Li, W. (2026). Secondary students’ reading of socio-scientific image-texts on climate change in a GPT-4 scenario. Research in Science Education, 56(1), 183–202. https://doi.org/10.1007/s11165-025-10258-w
Rappa, N. A., Nonis, K. P., Tang, K.-S., Cooper, G., Cooper, M., & Sims, C. (2026). Can generative AI support the learning agency of students with disability? A case study of an Australian secondary school. British Journal of Educational Technology, 57(4), 924–942. https://doi.org/10.1111/bjet.70048
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Shankar, S. K., Pothancheri, G., Sasi, D., & Mishra, S. (2025). Bringing teachers in the loop: Exploring perspectives on integrating generative AI in technology-enhanced learning. International Journal of Artificial Intelligence in Education, 35(1), 155–180. https://doi.org/10.1007/s40593-024-00428-8
Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14. https://doi.org/10.3102/0013189X015002004
Steinberg, L. (2005). Cognitive and affective development in adolescence. Trends in Cognitive Sciences, 9(2), 69–74. https://doi.org/10.1016/j.tics.2004.12.005
Tang, K.-S., & Putra, G. B. S. (2026). Generative AI as a dialogic partner: Enhancing multiple perspectives, reasoning, and argumentation in science education with customized chatbots. Journal of Science Education and Technology, 35(1), 128–140. https://doi.org/10.1007/s10956-025-10240-1
Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8, Article 45. https://doi.org/10.1186/1471-2288-8-45
Tipple, C., Keane, T., Wang, T., & Cerovac, M. (2026). Teachers’ perceptions of generative AI in gender-inclusive STEM education: A grounded theory study. Education and Information Technologies, 31(8), 2399–2434. https://doi.org/10.1007/s10639-026-13908-2
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), Article 010149. https://doi.org/10.1103/PhysRevPhysEducRes.21.010149
Tong, D., Tao, Y., Zhang, K., Dong, X., Hu, Y., Pan, S., & Liu, Q. (2024). Investigating ChatGPT-4’s performance in solving physics problems and its potential implications for education. Asia Pacific Education Review, 25(5), 1379–1389. https://doi.org/10.1007/s12564-023-09913-6
Tschisgale, P., Maus, H., Kieser, F., Kroehs, B., Petersen, S., & Wulff, P. (2025). Evaluating GPT- and reasoning-based large language models on physics olympiad problems: Surpassing human performance and implications for educational assessment. Physical Review Physics Education Research, 21(2), Article 020115. https://doi.org/10.1103/6fmx-bsnl
Valeri, F., Nilsson, P., & Cederqvist, A.-M. (2025). Exploring students’ experience of ChatGPT in STEM education. Computers and Education: Artificial Intelligence, 8, Article 100360. https://doi.org/10.1016/j.caeai.2024.100360
Wattanakasiwich, P., Kaewkhong, K., & Katwibun, D. (2025). Physics instructors’ acceptance and implementation of generative AI. Physical Review Physics Education Research, 21(1), Article 010155. https://doi.org/10.1103/r2fn-kdy4
Xu, S., Reiss, M. J., & Lodge, W. (2025). Comprehensive Scientific Creativity Assessment (C-SCA): A new approach for measuring scientific creativity in secondary school students. International Journal of Science and Mathematics Education, 23(2), 293–319. https://doi.org/10.1007/s10763-024-10469-z
Xu, Y., Liu, L., Xiong, J., & Zhu, G. (2025). Graders of the future: Comparing the consistency and accuracy of GPT4 and pre-service teachers in physics essay question assessments. Journal of Baltic Science Education, 24(1), 187–207. https://doi.org/10.33225/jbse/25.24.187
Yusuf, A., Pervin, N., Román-González, M., & Md Noor, N. (2024). Generative AI in education and research: A systematic mapping review. Review of Education, 12(2), Article e3489. https://doi.org/10.1002/rev3.3489
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2