Mapping Two Decades of Artificial Intelligence Research in Science Education: A Bibliometric Perspective (2000–2024)
Main Article Content
Abstract
This study aims to map the global trend of publications related to artificial intelligence (AI) in science learning through bibliometric analysis. The main problem raised is the limited understanding of the development of AI research themes in education, especially in developing countries. The research method uses bibliometric analysis with data taken from the Scopus database and analyzed using VOSviewer software. A total of 118 relevant articles from 2000 to 2024 were analyzed based on publication patterns, collaborations between researchers, and geographical distribution. The results of the study show a significant increase in the number of publications since the mid-2010s, with a peak in 2022. The most dominant topics include adaptive learning, learning analytics, and virtual simulation. Visualization of the collaborative network indicates high fragmentation, although there are several key authors and institutions leading the research. The study also found that most of the publications came from developed countries, with little representation from developing countries. The conclusion of the study confirms that AI has great potential for educational transformation, but its implementation is still limited by technology and resource gaps. The limitations of this study include a lack of data from developing countries and a lack of exploration of the pedagogical impact of AI. Therefore, it is recommended to expand international collaborations and enhance research that focuses on local contexts. In addition, an in-depth study of ethical and sustainability aspects in the application of AI in education is needed
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial — You may not use the material for commercial purposes.
-
NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
Aisyah, S., Najib, M., Rahmanita, B. N., & Prastowo, A. (2023). Drill and Practice Berbasis Video terhadap Efektivitas Belajar Matematika Siswa di Sekolah Dasar Negeri 15 Palembang. AR-RIAYAH : Jurnal Pendidikan Dasar, 7(1), 45–60. https://doi.org/10.29240/jpd.v7i1.6063
Alizadeh, F. (2020). eXplainable AI: Take one Step Back, Move two Steps forward: Investigating Folk Theories and Users’ Perception of Artificial Intelligence. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft Fur Informatik (GI). https://doi.org/10.18420/muc2020-ws111-369
Asuncion, A. (2009). Asynchronous distributed learning of topic models. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference.
Aswirna, P., Kiswanda, V., Nurhasnah, N., & Fahmi, R. (2022). Implementation of STEM E-Module with SDGs Principle to Improve Science Literacy and Environment-friendly Attitudes in Terms of Gender. JTK (Jurnal Tadris Kimiya), 7(1), 64–77. https://doi.org/10.15575/jtk.v7i1.16599
Aveleyra, E. E. (2023). Artificial intelligence in Physics teaching. Proceedings of the LACCEI International Multi-Conference for Engineering, Education and Technology, 2023. https://api.elsevier.com/content/abstract/scopus_id/85172304596
Baggio, B. (2020). AI and education reborn. ICSIT 2020 - 11th International Conference on Society and Information Technologies, Proceedings.
Baillifard, A. (2024). Effective learning with a personal AI tutor: A case study. Education and Information Technologies, Query date: 2024-11-30 11:25:32. https://doi.org/10.1007/s10639-024-12888-5
Bellas, F., Guerreiro-Santalla, S., Naya, M., & Duro, R. J. (2022). AI Curriculum for European High Schools: An Embedded Intelligence Approach. International Journal of Artificial Intelligence in Education, 33(2), 399–426. https://doi.org/10.1007/s40593-022-00315-0
Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18(1), 50. https://doi.org/10.1186/s41239-021-00282-x
Castro, R. (2009). Human active learning. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference.
Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two Decades of Artificial Intelligence in Education: Contributors, Collaborations, Research Topics, Challenges, and Future Directions. Educational Technology & Society, 25(1), 28–47.
Chin, D. B. (2010). Preparing students for future learning with Teachable Agents. Educational Technology Research and Development, 58(6), 649–669. https://doi.org/10.1007/s11423-010-9154-5
Cho, E. (2020). Trends of Educational Technology in Korea and the U.S.: A Report on the AECT-Korean Society for Educational Technology (KSET) Panel Discussion. TechTrends, 64(3), 357–360. https://doi.org/10.1007/s11528-020-00493-5
D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003
Doroudi, S. (2000). Where’s the Reward?: A Review of Reinforcement Learning for Instructional Sequencing. International Journal of Artificial Intelligence in Education, 29(4), 568–620. https://doi.org/10.1007/s40593-019-00187-x
Doroudi, S. (2023). The Intertwined Histories of Artificial Intelligence and Education. International Journal of Artificial Intelligence in Education, 33(4), 885–928. https://doi.org/10.1007/s40593-022-00313-2
Eaton, W. M., Burnham, M., Robertson, T., Arbuckle, J. G., Brasier, K. J., Burbach, M. E., Church, S. P., Hart-Fredeluces, G., Jackson-Smith, D., Wildermuth, G., Canfield, K. N., Córdova, S. C., Chatelain, C. D., Fowler, L. B., Hendawy, M. M. Z. elAbdeen, Kirchhoff, C. J., Manheim, M. K., Martinez, R. O., Mook, A., … Rogers, A. (2022). Advancing the scholarship and practice of stakeholder engagement in working landscapes: A co-produced research agenda. Socio-Ecological Practice Research, 4(4), 283–304. https://doi.org/10.1007/s42532-022-00132-8
Eck, N. van, & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. https://doi.org/10.1007/s11192-009-0146-3
Fowler, S., Cutting, C., Kennedy, J., Leonard, S. N., Gabriel, F., & Jaeschke, W. (2021). Technology enhanced learning environments and the potential for enhancing spatial reasoning: A mixed methods study. Mathematics Education Research Journal, 34(4), 887–910. https://doi.org/10.1007/s13394-021-00368-9
Goel, A. K. (2017). Using AI to teach AI: Lessons from an online AI class. AI Magazine, 38(2), 48–58. https://doi.org/10.1609/aimag.v38i2.2732
Hamal, O. (2022). Artificial Intelligent in Education. Sustainability (Switzerland), 14(5). https://doi.org/10.3390/su14052862
Hickmott, D., Prieto-Rodriguez, E., & Holmes, K. (2017). A Scoping Review of Studies on Computational Thinking in K–12 Mathematics Classrooms. Digital Experiences in Mathematics Education, 4(1), 48–69. https://doi.org/10.1007/s40751-017-0038-8
Holmes, G. (1994). WEKA: A machine learning workbench. Australian and New Zealand Conference on Intelligent Information Systems - Proceedings.
Järvelä, S. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057–1076. https://doi.org/10.1111/bjet.13325
Jiménez, Y. (2021). Artificial Intelligence in Neuroeducation: The Influence of Emotions in the Learning Science. Advances in Intelligent Systems and Computing, 1277. https://doi.org/10.1007/978-3-030-60467-7_6
Knight, S. (2018). Augmenting formative writing assessment with learning analytics: A design abstraction approach. Proceedings of International Conference of the Learning Sciences, ICLS, 3(2018), 1783–1790.
Koedinger, K. R. (2016). An Interview Reflection on “intelligent Tutoring Goes to School in the Big City.” International Journal of Artificial Intelligence in Education, 26(1), 13–24. https://doi.org/10.1007/s40593-015-0082-8
Kuzior, A., & Sira, M. (2022). A Bibliometric Analysis of Blockchain Technology Research Using VOSviewer. Sustainability, 14(13), Article 13. https://doi.org/10.3390/su14138206
Luckin, R. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824–2838. https://doi.org/10.1111/bjet.12861
Luckin, R., & Holmes, W. (2016). Intelligence Unleashed: An argument for AI in Education. In UCL Knowledge Lab: London, UK. [Report]. UCL Knowledge Lab. https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf
Martin, D. B. (2006). Mathematics Learning and Participation as Racialized Forms of Experience: African American Parents Speak on the Struggle for Mathematics Literacy. Mathematical Thinking and Learning, 8(3), 197–229. https://doi.org/10.1207/s15327833mtl0803_2
McLaren, B. M. (2011). Polite web-based intelligent tutors: Can they improve learning in classrooms? Computers & Education, 56(3), 574–584. https://doi.org/10.1016/j.compedu.2010.09.019
Mechelen, M. V. (2023). Emerging Technologies in K-12 Education: A Future HCI Research Agenda. ACM Transactions on Computer-Human Interaction, 30(3). https://doi.org/10.1145/3569897
Michael, J. (2001). In pursuit of meaningful learning. American Journal of Physiology - Advances in Physiology Education, 25(1), 145–158. https://doi.org/10.1152/advances.2001.25.3.145
Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100050
Molenaar, I. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139. https://doi.org/10.1016/j.chb.2022.107540
Nazari, N. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5). https://doi.org/10.1016/j.heliyon.2021.e07014
Nazario, A. P., de Lima, V. F., Fonseca, L. M. M., Leite, A. M., & Scochi, C. G. S. (2021). Development and evaluation of an educational video for families on the relief of acute pain in babies. Revista Gaúcha de Enfermagem, 42, e20190386. https://doi.org/10.1590/1983-1447.2021.20190386
Niels Pinkwart & Sannyuya Liu (Eds.). (2020). Artificial Intelligence Supported Educational Technologies (pub.1127241249). https://doi.org/10.1007/978-3-030-41099-5
Norbert M. Seel (Ed.). (2012). Encyclopedia of the Sciences of Learning (pub.1034155265). https://doi.org/10.1007/978-1-4419-1428-6
Roschelle, J. (2021). Intelligence Augmentation for Collaborative Learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12792(Query date: 2024-11-30 11:25:32), 254–264. https://doi.org/10.1007/978-3-030-77857-6_17
Salas-Pilco, S. Z. (2020). The impact of AI and robotics on physical, social-emotional and intellectual learning outcomes: An integrated analytical framework. British Journal of Educational Technology, 51(5), 1808–1825. https://doi.org/10.1111/bjet.12984
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Usak, M. (2024). Artificial Intelligence In Biology Education. Journal of Baltic Science Education, 23(5), 806–808. https://doi.org/10.33225/jbse/24.23.806
Wang, C. (2025). Application of artificial intelligence for feature engineering in education sector and learning science. Alexandria Engineering Journal, 110, 108–115. https://doi.org/10.1016/j.aej.2024.09.100
Wen, Q. (2024). Analysis of the Current Research Status of Intelligent Education in China Based on Knowledge Graph. Proceedings - 2024 6th International Conference on Computer Science and Technologies in Education, CSTE 2024, 56–61. https://doi.org/10.1109/CSTE62025.2024.00017
Wilton, L. (2022). Where Is the AI? AI Literacy for Educators. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13356(Query date: 2024-11-30 11:25:32), 180–188. https://doi.org/10.1007/978-3-031-11647-6_31
Yannier, N. (2020). Active Learning is About More Than Hands-On: A Mixed-Reality AI System to Support STEM Education. International Journal of Artificial Intelligence in Education, 30(1), 74–96. https://doi.org/10.1007/s40593-020-00194-3
Yasmin B. Kafai, William A. Sandoval, Noel Enyedy, Althea Scott Nixon, & Francisco Herrera (Eds.). (2012). Embracing Diversity in the Learning Sciences (pub.1021350905). https://doi.org/10.4324/9781410611017
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0