The impact of artificial intelligence on scientific practices: an emergent area of research for science education
Open accessAbstract
Artificial intelligence (AI) is now a major driver of societal acceleration making a significant impact on science and science education.AI is used by scientists to generate hypotheses, design experiments, collect and interpret data in ways that were not previously possible with traditional methods alone.Science education research is increasingly paying attention to the role of AI in teaching and learning.However, a significant gap in the emerging science education literature on AI concerns the impact of AI on scientific practices themselves, and implications such impact for science education.The article uses the NRC (2012.A framework for K-12 science education: practices, crosscutting concepts, and core ideas.National Academies Press.)framework of 'scientific practices' to trace example uses of AI in scientific practices and raises questions for science education.The questions relate to the relevance of AI-informed scientific practices for science curriculum, teaching and teacher education at the secondary level.The ultimate purpose of the article is to highlight that the sooner the role of AI on scientific practices are researched and applied in science education policy and practice, the less likely that education will become outdated in helping students thrive in the fast changing landscape of scientific research.
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Applications of computer vision in K-12 education include classroom surveillance, automatic attendance, handwriting recognition, generation of visual content, and science laboratory tasks.
“Applications of computer vision in K-12 education include classroom surveillance, automatic attendance, handwriting recognition, generation of visual content, and science laboratory tasks [1,39-41].”
From Adoption of generative artificial intelligence in K-12 education: Teacher preparedness, ethical concerns, and implementation challenges (2026) · cites this paper as [1]
The Introduction discusses AI applications in primary and secondary science education, including automation and detection or prediction, but does not identify them as computer-vision applications or describe the listed uses. Searches for classroom surveillance, attendance, handwriting recognition, visual-content generation, and laboratory tasks found no passage addressing those uses. (LLM, conf=0.74)
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Cites
(2013) · Cited by 6,708 · see citing works
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence
Cooper, G. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10039-y · Cited by 1,263 · see citing works
Beyond Shielding: The Roles of Glycans in the SARS-CoV-2 Spike Protein
Casalino, L., Gaieb, Z., Goldsmith, J. A., Hjorth, C. K., Dommer, A. C., Harbison, A. M., Fogarty, C. A., de Barros, E. P., Taylor, B. C., McLellan, J. S., et al. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c01056 · Cited by 909 · see citing works
Floridi, L. (2014) · Springer eBooks · doi:10.1007/978-3-319-04093-6 · Cited by 709 · see citing works
Epistemologies in practice: Making scientific practices meaningful for students
Berland, L., Schwarz, C., Krist, C., Kenyon, L., Lo, A., & Reiser, B. (2016) · Journal of Research in Science Teaching · doi:10.1002/tea.21257 · Cited by 508 · see citing works
TPACK in the age of ChatGPT and Generative AI
Mishra, P., Warr, M., & Islam, R. (2023) · Journal of Digital Learning in Teacher Education · doi:10.1080/21532974.2023.2247480 · Cited by 484 · see citing works
Generative AI in Education and Research: Opportunities, Concerns, and Solutions
Alasadi, E. A., & Baiz, C. R. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00323 · Cited by 463 · see citing works
Advancing mathematics by guiding human intuition with AI
Davies, A., Veličković, P., Buesing, L., Blackwell, S., Zheng, D., Tomašev, N., Tanburn, R., Battaglia, P., Blundell, C., Juhász, A., et al. (2021) · Nature · doi:10.1038/s41586-021-04086-x · Cited by 454 · see citing works
The Onlife Manifesto: Being Human in a Hyperconnected Era
Miskiewicz, W. A. (2015) · Library Review · doi:10.1108/lr-04-2015-0034 · Cited by 338 · see citing works
Data-driven materials research enabled by natural language processing and information extraction
Olivetti, E. A., Cole, J. M., Kim, E., Kononova, O., Ceder, G., Han, T. Y.-J., & Hiszpanski, A. M. (2020) · Applied Physics Reviews · doi:10.1063/5.0021106 · Cited by 302 · see citing works
The transformative potential of artificial intelligence
Gruetzemacher, R., & Whittlestone, J. (2021) · Futures · doi:10.1016/j.futures.2021.102884 · Cited by 262 · see citing works
The Future of Education and Skills
OECD (2018) · Education policy pointers · doi:10.1787/54ac7020-en · Cited by 231 · see citing works
A systematic review of teaching and learning machine learning in K-12 education
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11416-7 · Cited by 212 · see citing works
Investigating the Use of an Artificial Intelligence Chatbot with General Chemistry Exam Questions
Clark, T. M. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00027 · Cited by 176 · see citing works
Artificial Intelligence in Science Education (2013–2023): Research Trends in Ten Years
Jia, F., Sun, D., & Looi, C. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-023-10077-6 · Cited by 170 · see citing works
What's next for AlphaFold and the AI protein-folding revolution
Callaway, E. (2022) · Nature · doi:10.1038/d41586-022-00997-5 · Cited by 146 · see citing works
Machine learning for middle schoolers: Learning through data-driven design
Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2021) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2021.100281 · Cited by 135 · see citing works
Phillips, L. M., & Norris, S. P. (2009) · Research in Science Education · doi:10.1007/s11165-008-9111-z · Cited by 116 · see citing works
Rediscovering orbital mechanics with machine learning
Lemos, P., Jeffrey, N., Cranmer, M., Ho, S., & Battaglia, P. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acfa63 · Cited by 90 · see citing works
Graph similarity drives zeolite diffusionless transformations and intergrowth
Schwalbe‐Koda, D., Jensen, Z., Olivetti, E., & Gómez‐Bombarelli, R. (2019) · Nature Materials · doi:10.1038/s41563-019-0486-1 · Cited by 80 · see citing works
Assigning confidence to molecular property prediction
Nigam, A., Pollice, R., Hurley, M. F. D., Hickman, R. J., Aldeghi, M., Yoshikawa, N., Chithrananda, S., Voelz, V. A., & Aspuru‐Guzik, A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1925247 · Cited by 68 · see citing works
AI is transforming how science is done. Science education must reflect this change.
Erduran, S. (2023) · Science · doi:10.1126/science.adm9788 · Cited by 62 · see citing works
Self-citationInterview with the Chatbot: How Does It Reason?
Talanquer, V. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00472 · Cited by 56 · see citing works
Humanising the nature of science: an analysis of the science curriculum in Norway
Mork, S. M., Haug, B. S., Sørborg, Ø., Ruben, S. P., & Erduran, S. (2022) · International Journal of Science Education · doi:10.1080/09500693.2022.2088876 · Cited by 47 · see citing works
Self-citationScience Teachers’ Views of Argument in Scientific Inquiry and Argument-Based Science Instruction
Choi, A., Seung, E., & Kim, D.-E. (2019) · Research in Science Education · doi:10.1007/s11165-019-9861-9 · Cited by 36 · see citing works
Method of the Year 2021: Protein structure prediction
(2022) · Nature Methods · doi:10.1038/s41592-021-01380-4 · Cited by 32 · see citing works
Hypotheses devised by AI could find ‘blind spots’ in research
Hutson, M. (2023) · Nature · doi:10.1038/d41586-023-03596-0 · Cited by 24 · see citing works
Ethical and Responsible Use of AI/ML in the Earth, Space, and Environmental SciencesPreprint
Stall, S., Cervone, G., Coward, C., Cutcher‐Gershenfeld, J., Donaldson, T. J., Erdmann, C. C., Hanson, B., Holm, J., King, J. L., Lyon, L., et al. (2023) · doi:10.22541/essoar.168132856.66485758/v1 · Cited by 19 · see citing works
Scientific Practices in Science Education Publications: An Analysis of Research Contexts
Costa, S. L. R., & Broietti, F. C. D. (2021) · Science education international · doi:10.33828/sei.v32.i4.1 · Cited by 9 · see citing works
School Science Practical Work in Africa
(2020) · Routledge eBooks · doi:10.4324/9780429260650 · Cited by 8 · see citing works
The scientist’s ways in national science curricula: A comparative study between Taiwan and Vietnam
Nguyen, V.-H., Cheng, P.-H., Chien, Y.-H., & Chang, C. (2023) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/13753 · Cited by 5 · see citing works
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Research works printed but not linked: 1
- National Research Council. (2012). A framework for K-12 science education: Practices, crosscut- ting concepts, and core ideas. National Academies Press.
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Artificial Intelligence in Chemistry Research─Implications for Teaching and Learning
Berber, S., Brückner, M., Maurer, N., & Huwer, J. (2025) · Journal of Chemical Education · doi:10.1021/acs.jchemed.4c01033 · Cited by 50 · see citing works
Cheung, K. K. C., Zerouali, A., Koenen, J., & Erduran, S. (2025) · Studies in Science Education · doi:10.1080/03057267.2025.2578091 · Cited by 31 · see citing works
Self-citationUsher, M., Barak, M., & Erduran, S. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00567-x · Cited by 21 · see citing works
Self-citationCheung, K. K. C., Pun, J., Kenneth-Li, W., & Mai, J. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10182-0 · Cited by 14 · see citing works
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Fayzullina, A. R., Filippova, A. A., N.Yu., G., Astakhov, D. V., Александровна, К. Н., & Zaripova, Z. F. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/17519 · Cited by 14 · see citing works
Alé-Silva, J., Ávalos, B., & Araya, R. (2025) · Review of Education · doi:10.1002/rev3.70098 · Cited by 27 · see citing works
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Kumar, J. A., Zhuang, M., & Thomas, S. R. (2024) · Natural sciences education · doi:10.1002/nse2.70003 · Cited by 18 · see citing works
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Dağdelen, E., Güngör, H., & Ünal, İ. (2025) · İnönü Üniversitesi Eğitim Fakültesi Dergisi · doi:10.17679/inuefd.1650862 · Cited by 5 · see citing works
Zhou, C. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1830646 · Cited by 4 · see citing works
Yacobson, E., Schleifer, Y., Bar-Dov, Z., Rap, S., Blonder, R., & Alexandron, G. (2026) · Journal of Science Education and Technology · doi:10.1007/s10956-026-10310-y · Cited by 6 · see citing works
Lewenstein, B. V., & Baram‐Tsabari, A. (2024) · International Journal of Science Education Part B · doi:10.1080/21548455.2024.2423069 · Cited by 3 · see citing works
Qablan, A. M., Badawy, H. R. I., Badawy, H. R. I., & Maradian, P. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0573-8.ch002 · Cited by 3 · see citing works
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Self-citationAnđić, B., Helm, C., Weinhandl, R., Maričić, M., Radlmair, A., Bleckenwegner, V., & Ebner, M. (2026) · Research in Science Education · doi:10.1007/s11165-026-10359-0 · Cited by 2 · see citing works
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Efe, R., & Efe, H. A. (2025) · Journal of Baltic Science Education · doi:10.33225/jbse/25.24.655 · Cited by 1 · see citing works
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Chan, H. Y., & Erduran, S. (2026) · Research in Science & Technological Education · doi:10.1080/02635143.2026.2669549 · Cited by 1 · see citing works
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Lee, M., Lin, T., Chng, E., Chiou, G., Tan, A., Liang, J., Huang, P.-C., Tan, S. C., & Tsai, C. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105756 · Cited by 1 · see citing works
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Ayanda, A. O. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0203003
“Generative artificial intelligence, also known as GenAI, has been coined as offering new prospects of individualized learning, automated feedback, lesson planning, instructional design, assessment generation, as well as student engagement [1].”
Influential1. Introduksjon til forskningsbasert lærerutdanning i naturfag og prosjekt TRELIS
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Farina, M., Yu, X., Ji, R., Chen, J., Blok, V., Ciancarini, P., Busani, N., & Pedrycz, W. (2025) · AI & Innovation · doi:10.1002/aiv2.70000
Sahin, B., & Wade‐Jaimes, K. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09909-8
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Simmie, G. M. (2025) · Springer eBooks · doi:10.1007/978-3-032-07769-1_7
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Cheung, K. K. C., Enzo, C. J., Cho, Y. H., Wan, Z. H., Wai, C. H., & Fung, P. W. (2026) · Research in Science Education · doi:10.1007/s11165-026-10366-1 · Cited by 1 · see citing works
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Maurer, N., & Huwer, J. (2026) · Journal of Chemical Education · doi:10.1021/acs.jchemed.6c00128 · Cited by 1 · see citing works
Distributed Cognition in AI-Mediated Learning Environments for Self-Directed Independent Learners
Alam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7225-9.ch003
Li, L., Liu, J., Yeung, S.-K., Looi, C.-K., Zhao, Y., & Sun, D. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100673
Initiation to Science Alongside Generative AI: Preserving Epistemic Integrity
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Mesci, G., Zhaı, X., & Capps, D. K. (2026) · Research in Science & Technological Education · doi:10.1080/02635143.2026.2731035
Introducing large language models (LLMs) in physics education: A module for future teachers
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y Yovera, S. E. R., Lázaro, R. A. S., Villanueva, Y. D. L. E., & Villarreal, L. A. B. (2026) · Revista Venezolana de Gerencia · doi:10.52080/rvgluz.31.116.12
Rodríguez-Montoya, C., Garrido, L. E., & VanderLinde, G. (2026) · Discover Education · doi:10.1007/s44217-026-02175-5 · Cited by 2 · see citing works
A Structural Model of Students’ Science Identity in the Context of GenAI
Cheung, K. K. C., & Zhang, W. (2026) · International Journal of Science and Mathematics Education · doi:10.1007/s10763-026-10723-6
Sihombing, R. A., Liu, S.-Y., Kusmahardhika, N., & Chang, C. (2026) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/19279
Kannaoui, S., Elwahab, F., Derkaoui, N. E. H., Brhadda, N., Loukili, A., & Ziri, R. (2026) · Discover Education · doi:10.1007/s44217-026-02208-z
Sarici, E., & Elmas, R. (2026) · Turkiye Kimya Dernegi Dergisi Kisim C Kimya Egitimi · doi:10.37995/jotcsc.2000157
Martin, P. P., Rost, M., Koenen, J., & Graulich, N. (2026) · Science & Education · doi:10.1007/s11191-026-00789-7
Choi, Y.-S. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1963429
Emergence and development of observation practices in AI-assisted plant adaptation learning
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GÖKTAŞ, O. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1542.c6239
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- Authors
- Erduran, S., & Levrini, O.
- Affiliations
- University of Oxford (GB)
- University of Bologna (IT)
- Corresponding author
- Erduran, Sibel
- Venue
- International Journal of Science Education
- Publisher
- Informa UK Limited
- Published
- 2024-02-04
- Volume
- 46
- Issue
- 18
- Pages
- 1982-1989
- ISSN
- 0950-0693 (ISSN-L)
- 1464-5289
- OpenAlex
- W4391538253
- Cited by
- 92
- References
- 33
- Citations by year
- 2026: 53 · 2025: 29 · 2024: 10
- FWCI
- 116.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Science Education (publishedVersion, cc-by-nc-nd)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, scientific practice, artificial intelligence in science, K-12 science education, science curriculum, teacher professional development, secondary science teaching, science education policy
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