Abstract
The use of artificial intelligence has played an important role in science teaching and learning. The purpose of this study was to fill a gap in the current review of research on AI in science education (AISE) in the early stage of education by systematically reviewing existing research in this area. This systematic review examined the trends and research foci of AI in the science of early stages of education. This review study employed a bibliometric analysis and content analysis to examine the characteristics of 76 studies on Artificial Intelligence in Science Education (AISE) indexed in Web of Science and Scopus from 2013 to 2023. The analytical tool CiteSpace was utilized for the analysis. The study aimed to provide an overview of the development level of AISE and identify major research trends, keywords, research themes, high-impact journals, institutions, countries/regions, and the impact of AISE studies. The results, based on econometric analyses, indicate that AISE has experienced increasing influence over the past decade. Cluster and timeline analyses of the retrieved keywords revealed that AI in primary and secondary science education can be categorized into 11 main themes, and the chronology of their emergence was identified. Among the most prolific journals in this field are the International Journal of Social Robotics, Educational Technology Research and Development, and others. Furthermore, the analysis identified that institutions and countries/regions located primarily in the United States have made the most significant contributions to AISE research. To explore the learning outcomes and overall impact of AI technologies on learners in primary and secondary schools, content analysis was conducted, identifying five main categories of technology applications. This study provides valuable insights into the advancements and implications of AI in science education at the primary and secondary levels.
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References
Akgun, S., Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7
Aktoprak, A., & Hursen, C. (2022). A bibliometric and content analysis of critical thinking in primary education. Thinking Skills and Creativity, 44. https://doi.org/10.1016/j.tsc.2022.101029
Alam, A. (2022). A digital game based learning approach for effective curriculum transaction for teaching-learning of artificial intelligence and machine learning. Paper presented at the 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 69–74.
Aldabe, I., & Maritxalar, M. (2014). Semantic similarity measures for the generation of science tests in basque. IEEE Transactions on Learning Technologies, 7(4), 375–387.
Ali, S., Payne, B. H., Williams, R., Park, H. W., & Breazeal, C. (2019). Constructionism, ethics, and creativity: Develo** primary and middle school artificial intelligence education. Paper presented at the International Workshop on Education in Artificial Intelligence K-12 (eduai’19), 2 1–4.
Almeda, M. V., & Baker, R. S. (2020). Predicting student participation in STEM careers: The role of affect and engagement during middle school. Journal of Educational Data Mining, 12(2), 33–47. https://doi.org/10.5281/zenodo.4008054
Amo, D., Fox, P., Fonseca, D., & Poyatos, C. (2020). Systematic review on which analytics and learning methodologies are applied in primary and secondary education in the learning of robotics sensors. Sensors (Basel, Switzerland), 21(1), 153. https://doi.org/10.3390/s21010153
Avsec, S., Rihtarsic, D., & Kocijancic, S. (2014). A predictive study of learner attitudes toward open learning in a robotics class. Journal of Science Education and Technology, 23, 692–704.
Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484.
Bernstein, D., Puttick, G., Wendell, K., Shaw, F., Danahy, E., & Cassidy, M. (2022). Designing biomimetic robots: Iterative development of an integrated technology design curriculum. Educational Technology Research and Development, 70(1), 119–147. https://doi.org/10.1007/s11423-021-10061-0
Bertalanffy, L. (1968). General systems theory as integrating factor in contemporary science. Akten Des XIV Internationalen Kongresses Für Philosophie, 2, 335–340.
Bertram, C., Weiss, Z., Zachrich, L., & Ziai, R. (2021). Artificial intelligence in history education. Linguistic content and complexity analyses of student writings in the CAHisT project (computational assessment of historical thinking). Computers and Education: Artificial Intelligence, 100038.
Biehler, R., & Fleischer, Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter Notebooks. Teaching Statistics, 43, S133–S. https://doi.org/10.1111/test.12279
Çetinkaya, A., & Baykan, Ö. K. (2020a). Prediction of middle school students’ programming talent using artificial neural networks. Engineering Science and Technology an International Journal, 23(6), 1301–1307. https://doi.org/10.1016/j.jestch.2020.07.005
Çetinkaya, A., & Baykan, Ö. K. (2020b). Prediction of middle school students’ programming talent using artificial neural networks. Engineering Science and Technology an International Journal, 23(6), 1301–1307. https://doi.org/10.1016/j.jestch.2020.07.005
Cheah, C. W. (2021). Develo** a gamified AI-enabled online learning application to improve students’ perception of university physics. Computers and Education: Artificial Intelligence, 2, 100032.
Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences, 101(suppl_1), 5303–5310.
Chen, C. (2016). CiteSpace: A practical guide for map** scientific literature. Nova Science Publishers Hauppauge.
Chen, C. (2017). Science map**: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40.
Chen, J., & See, K. C. (2020). Artificial intelligence for COVID-19: Rapid review. Journal of Medical Internet Research, 22(10), e21476.
Chen, D., & Stroup, W. (1993). General system theory: Toward a conceptual framework for science and technology education for all. Journal of Science Education and Technology, 2, 447–459.
Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012). Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opinion on Biological Therapy, 12(5), 593–608.
Chen, Y., Chen, C. M., Liu, Z. Y., Hu, Z. G., & Wang, X. W. (2015). The methodology function of CiteSpace map** knowledge domains. Studies in Science of Science, 33(2), 242–253.
Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers and Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001
Chen, X., Zhang, X., **e, H., Wang, F.L., Yan, J., & Hao, T. (2019). Trends and features of human brain research using artificial intelligence techniques: A bibliometric approach. In A. Zeng, D. Pan, T. Hao, D. Zhang, Y. Shi, & X. Song (Eds.), Human brain and artificial intelligence. HBAI 2019. Communications in computer and information science, Vol. 1072. Springer. https://doi.org/10.1007/978-981-15-1398-5_5
Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. routledge.
Crawford, M. H. (1974). Roman republican coinage. Cambridge University Press.
Cutumisu, M., Blair, K. P., Chin, D. B., & Schwartz, D. L. (2017). Assessing whether students seek constructive criticism: The design of an automated feedback system for a Graphic Design Task. International Journal of Artificial Intelligence in Education, 27(3), 419–447. https://doi.org/10.1007/s40593-016-0137-5
Dede, C., Grotzer, T. A., Kamarainen, A., & Metcalf, S. (2017). EcoXPT: Designing for deeper learning through experimentation in an immersive virtual ecosystem. Journal of Educational Technology & Society, 20(4), 166–178.
Dettweiler, U., Lauterbach, G., Becker, C., & Simon, P. (2017). A bayesian mixed-methods analysis of basic psychological needs satisfaction through outdoor learning and its influence on motivational behavior in science class. Frontiers in Psychology, 2235.
Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241–6265. https://doi.org/10.1007/s10639-021-10627-8
Di Eugenio, B., Fossati, D., & Green, N. (2021). Intelligent support for computer science education: Pedagogy enhanced by artificial intelligence. CRC Press.
Dobrev, D. (2012). A definition of artificial intelligence. ar**v Preprint ar**v:12101568.
Dolenc, K., & Aberšek, B. (2015a). TECH8 intelligent and adaptive e-learning system: Integration into Technology and Science classrooms in lower secondary schools. Computers and Education, 82, 354–365. https://doi.org/10.1016/j.compedu.2014.12.010
Dolenc, K., & Aberšek, B. (2015b). TECH8 intelligent and adaptive e-learning system: Integration into Technology and Science classrooms in lower secondary schools. Computers & Education, 82, 354–365. https://doi.org/10.1016/j.compedu.2014.12.010
Dolenc, K., Aberšek, B., & Aberšek, M. K. (2015). Online functional literacy, intelligent tutoring systems and science education. Journal of Baltic Science Education, 14(2), 162–171.
Drack, M., & Pouvreau, D. (2015). On the history of Ludwig von Bertalanffy’s General Systemology, and on its relationship to cybernetics–part III: Convergences and divergences. International Journal of General Systems, 44(5), 523–571.
Drigas, A. S., & Ioannidou, R. (2013). A review on artificial intelligence in special education. Information Systems, E-Learning, and Knowledge Management Research: 4th World Summit on the Knowledge Society, WSKS 2011, Mykonos, Greece, September 21–23, 2011.Revised Selected Papers 4,, 385–391.
Eaton, E., Koenig, S., Schulz, C., Maurelli, F., Lee, J., Eckroth, J., Crowley, M., Freedman, R. G., Cardona-Rivera, R. E., & Machado, T. (2018). Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. AI Matters, 3(4), 23–31.
Elizabeth Casey, J., Gill, P., Pennington, L., & Mireles, S. V. (2018). Lines, roamers, and squares: Oh my! Using floor robots to enhance hispanic students’ understanding of programming. Education and Information Technologies, 23, 1531–1546.
Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology, 34(2), 133–139.
Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences. Taylor & Francis.
Gkiolnta, E., Zygopoulou, M., & Syriopoulou-Delli, C. (2023). Robot programming for a child with autism spectrum disorder: A pilot study. International Journal of Developmental Disabilities, 69(3), 424–431. https://doi.org/10.1080/20473869.2023.2194568
Göktepe Körpeoğlu, S., & Göktepe Yıldız, S. (2023). Comparative analysis of algorithms with data mining methods for examining attitudes towards STEM fields. Education and Information Technologies, 28(3), 2791–2826. https://doi.org/10.1007/s10639-022-11216-z
Gomoll, A., Šabanović, S., Tolar, E., Hmelo-Silver, C., Francisco, M., & Lawlor, O. (2018). Between the Social and the Technical: Negotiation of human-centered Robotics Design in a Middle School Classroom. International Journal of Social Robotics, 10(3), 309–324. https://doi.org/10.1007/s12369-017-0454-3
Hagger, M. S., & Hamilton, K. (2018). Motivational predictors of students’ participation in out-of-school learning activities and academic attainment in science: An application of the trans-contextual model using bayesian path analysis. Learning and Individual Differences, 67, 232–244.
Heintz, F. (2021). Three interviews about K-12 AI education in America, Europe, and Singapore. KI-Künstliche Intelligenz, 35(2), 233–237.
Holmes, W., Bialik, M., & Fadel, C. (2023a). Artificial intelligence in education. (). Globethics Publications.
Holmes, W., Bialik, M., & Fadel, C. (2023b). Artificial intelligence in education. (). Globethics Publications.
Hoorn, J. F., Huang, I. S., Konijn, E. A., & van Buuren, L. (2021). Robot tutoring of multiplication: Over one-third learning gain for most, learning loss for some. Robotics, 10(1), 1–24. https://doi.org/10.3390/robotics10010016
Hwang, G., **e, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001.
Jang, J., Jeon, J., & Jung, S. K. (2022). Development of STEM-Based AI education program for sustainable improvement of Elementary Learners. Sustainability, 14(22), 15178.
Järvelä, S., Nguyen, A., Vuorenmaa, E., Malmberg, J., & Järvenoja, H. (2023). Predicting regulatory activities for socially shared regulation to optimize collaborative learning. Computers in Human Behavior, 144. https://doi.org/10.1016/j.chb.2023.107737.
Jia, K., Wang, P., Li, Y., Chen, Z., Jiang, X., Lin, C., & Chin, T. (2022). Research landscape of artificial intelligence and e-learning: A bibliometric research. Frontiers in Psychology, 13, 795039.
Jiang, S., Huang, X., Sung, S. H., & **e, C. (2023). Learning analytics for assessing Hands-on Laboratory Skills in Science Classrooms using bayesian network analysis. Research in Science Education, 53(2), 425–444. https://doi.org/10.1007/s11165-022-10061-x
Julià, C., & Antolí, J. (2016). Spatial ability learning through educational robotics. International Journal of Technology and Design Education, 26(2), 185–203. https://doi.org/10.1007/s10798-015-9307-2
Kandlhofer, M., Steinbauer, G., Lassnig, J., Menzinger, M., Baumann, W., Ehardt-Schmiederer, M., Bieber, R., Winkler, T., Plomer, S., & Strobl-Zuchtriegl, I. (2021). EDLRIS: A european driving license for robots and intelligent systems. KI-Künstliche Intelligenz, 35, 221–232.
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36(20), 1902–1911.
Kitto, H. D. F. (2014). Form and meaning in drama: A study of six greek plays and of Hamlet. Routledge.
Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: Definition, trends, techniques, and cases. Artificial Intelligence, 1, 270–299.
Kong, F. (2020). Application of artificial intelligence in modern art teaching. International Journal of Emerging Technologies in Learning (iJET), 15(13), 238–251.
Kong, S., Cheung, W. M., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026.
Lee, H., Gweon, G., -., Lord, T., Paessel, N., Pallant, A., & Pryputniewicz, S. (2021). Machine learning-enabled automated feedback: Supporting students’ revision of scientific arguments based on data drawn from Simulation. Journal of Science Education and Technology, 30(2), 168–192. https://doi.org/10.1007/s10956-020-09889-7
Li, E., Li, S., & Yuan, X. (2022). Adoption and Perception of Artificial Intelligence Technologies by Children and Teens in Education. Paper presented at the International Conference on Human-Computer Interaction, 69–79.
Liang, J., Hwang, G., Chen, M. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: An integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 7, 4270–4296.
Liu, T. C. (2022). A case study of the adaptive learning platform in a Taiwanese Elementary School: Precision Education from Teachers’ perspectives. Education and Information Technologies, 27(5), 6295–6316. https://doi.org/10.1007/s10639-021-10851-2
Lu, W., Griffin, J., Sadler, T. D., Laffey, J., & Goggins, S. P. (2023a). Serious game analytics by design: Feature generation and selection using game Telemetry and Game Metrics: Toward predictive model construction. Journal of Learning Analytics, 10(1), 168–188. https://doi.org/10.18608/jla.2023.7681
Lu, W., Griffin, J., Sadler, T. D., Laffey, J., & Goggins, S. P. (2023b). Serious game analytics by design: Feature generation and selection using game Telemetry and Game Metrics: Toward predictive model construction. Journal of Learning Analytics, 10(1), 168–188. https://doi.org/10.18608/jla.2023.7681
Luo, F., Antonenko, P. D., & Davis, E. C. (2020). Exploring the evolution of two girls’ conceptions and practices in computational thinking in science. Computers & Education, 146. https://doi.org/10.1016/j.compedu.2019.103759.
Magana, A. J., Elluri, S., Dasgupta, C., Seah, Y. Y., Madamanchi, A., & Boutin, M. (2019). The role of simulation-enabled design learning experiences on middle school students’ self-generated inherence heuristics. Journal of Science Education and Technology, 28, 382–398.
Malakul, S., & Park, I. (2023). The effects of using an auto-subtitle system in educational videos to facilitate learning for secondary school students: learning comprehension, cognitive load, and satisfaction. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00224-2.
Martí-Parreño, J., Méndez‐Ibáñez, E., & Alonso‐Arroyo, A. (2016). The use of gamification in education: A bibliometric and text mining analysis. Journal of Computer Assisted Learning, 32(6), 663–676.
Martins, R. M., von Wangenheim, C. G., Rauber, M. F., & Hauck, J. C. (2023). Machine learning for all!—Introducing machine learning in middle and high school. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00325-y
Mazov, N. A., Gureev, V. N., & Glinskikh, V. N. (2020). The methodological basis of defining research trends and fronts. Scientific and Technical Information Processing, 47, 221–231.
Min, W., Frankosky, M. H., Mott, B. W., Rowe, J. P., Smith, A., Wiebe, E., Boyer, K. E., & Lester, J. C. (2020). DeepStealth: Game-based Learning Stealth Assessment with deep neural networks. IEEE Transactions on Learning Technologies, 13(2), 312–325. https://doi.org/10.1109/TLT.2019.2922356
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151(4), 264–269.
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1–9.
Nemiro, J., Larriva, C., & Jawaharlal, M. (2017). Develo** creative behavior in Elementary School Students with Robotics. Journal of Creative Behavior, 51(1), 70–90. https://doi.org/10.1002/jocb.87
Nguyen, A., Järvelä, S., Rosé, C., Järvenoja, H., & Malmberg, J. (2023). Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics. British Journal of Educational Technology, 54(1), 293–312. https://doi.org/10.1111/bjet.13280
Noh, J., & Lee, J. (2020). Effects of robotics programming on the computational thinking and creativity of elementary school students. Educational Technology Research and Development, 68(1), 463–484. https://doi.org/10.1007/s11423-019-09708-w
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925.
Park, H., & Shea, P. (2020). A review of Ten-Year Research through Co-citation Analysis: Online Learning, Distance Learning, and blended learning. Online Learning, 24(2), 225–244.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.
Pei, B., **ng, W., & Wang, M. (2021). Academic development of multimodal learning analytics: A bibliometric analysis. Interactive Learning Environments, 1–19.
Perrakis, A., & Sixma, T. K. (2021). AI revolutions in biology: The joys and perils of AlphaFold. EMBO Reports, 22(11), e54046.
Petersen, G. B., Mottelson, A., & Makransky, G. (2021). Pedagogical agents in educational vr: An in the wild study. Paper presented at the Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–12.
Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153.
Polyak, S. T., von Davier, A. A., & Peterschmidt, K. (2017). Computational psychometrics for the measurement of collaborative problem solving skills. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.02029.
Pou, A. V., Canaleta, X., & Fonseca, D. (2022). Computational Thinking and Educational Robotics Integrated into Project-Based Learning. Sensors, 22(10). https://doi.org/10.3390/s22103746.
Pritchard, A. (1969). Statistical Bibliography; An Interim Bibliography.
Qian, Y., & Lehman, J. (2018). Using technology to support teaching computer science: A study with middle school students. Eurasia Journal of Mathematics, Science and Technology Education, 14(12). https://doi.org/10.29333/ejmste/94227.
Rapoport, A. (1986). General system theory: Essential concepts & applications. CRC Press.
Rawat, K. S., & Sood, S. K. (2021). Knowledge map** of computer applications in education using CiteSpace. Computer Applications in Engineering Education, 29(5), 1324–1339.
Rosi, A., Dall’Asta, M., Brighenti, F., Del Rio, D., Volta, E., Baroni, I., Nalin, M., Coti Zelati, M., Sanna, A., & Scazzina, F. (2016). The use of new technologies for nutritional education in primary schools: A pilot study; 27756495. Public Health, 140, 50–55. https://doi.org/10.1016/j.puhe.2016.08.021
Rosvall, M., & Bergstrom, C. T. (2010). Map** change in large networks. PloS One, 5(1), e8694.
Sabharwal, A., & Selman, B. (2011). No title. S.Russell, P.Norvig, Artificial Intelligence: A Modern Approach,
Saha, S. K., & Rao, C. H., D (2022). Development of a practical system for computerized evaluation of descriptive answers of middle school level students. Interactive Learning Environments, 30(2), 215–228. https://doi.org/10.1080/10494820.2019.1651743
Salas-Pilco, S. (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
Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2013a). The effect of contextualized conversational feedback in a complex open-ended learning environment. Educational Technology Research and Development, 61(1), 71–89. https://doi.org/10.1007/s11423-012-9275-0
Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2013b). The effect of contextualized conversational feedback in a complex open-ended learning environment. Educational Technology Research and Development, 61, 71–89.
Shiomi, M., Kanda, T., Howley, I., Hayashi, K., & Hagita, N. (2015). Can a social robot stimulate science curiosity in classrooms? International Journal of Social Robotics, 7, 641–652.
Simmons, A. B., & Chappell, S. G. (1988). Artificial intelligence-definition and practice. IEEE Journal of Oceanic Engineering, 13(2), 14–42.
Sisman, B., Gunay, D., & Kucuk, S. (2019). Development and validation of an educational robot attitude scale (ERAS) for secondary school students. Interactive Learning Environments, 27(3), 377–388.
Sisman, B., Kucuk, S., & Yaman, Y. (2021). The Effects of Robotics Training on Children’s spatial ability and attitude toward STEM. International Journal of Social Robotics, 13(2), 379–389. https://doi.org/10.1007/s12369-020-00646-9
Small, H. (1999). Visualizing science by citation map**. Journal of the American Society for Information Science, 50(9), 799–813.
Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21, 473–486.
Su, J., & Zhong, Y. (2022). Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Computers and Education: Artificial Intelligence, 3, 100072.
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, 100124.
Tang, L., Li, J., & Fantus, S. (2023). Medical artificial intelligence ethics: A systematic review of empirical studies. Digital Health, 9, 20552076231186064.
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. Ieee Access : Practical Innovations, Open Solutions, 9, 110558–110572.
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI? Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence, 33(01) 9795–9799.
Trinidad, M., Ruiz, M., & Calderon, A. (2021). A bibliometric analysis of gamification research. Ieee Access : Practical Innovations, Open Solutions, 9, 46505–46544.
Üçgül, M., & Altıok, S. (2022). You are an astroneer: The effects of robotics camps on secondary school students’ perceptions and attitudes towards STEM. International Journal of Technology and Design Education, 32(3), 1679–1699. https://doi.org/10.1007/s10798-021-09673-7
Von Bertalanffy, L. (1950). An outline of general system theory. The British Journal for the Philosophy of Science, 1(2), 134–165.
Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37.
Ward, W., Cole, R., Bolaños, D., Buchenroth-Martin, C., Svirsky, E., & Weston, T. (2013). My science tutor: A conversational multimedia virtual tutor. Journal of Educational Psychology, 105(4), 1115–1125. https://doi.org/10.1037/a0031589
Witherspoon, E. B., Higashi, R. M., Schunn, C. D., Baehr, E. C., & Shoop, R. (2017). Develo** computational thinking through a virtual robotics programming curriculum. ACM Transactions on Computing Education, 18(1). https://doi.org/10.1145/3104982.
Witherspoon, E. B., Schunn, C. D., Higashi, R. M., & Shoop, R. (2018). Attending to structural programming features predicts differences in learning and motivation. Journal of Computer Assisted Learning, 34(2), 115–128.
Wu, S., & Yang, K. (2022). The effectiveness of teacher support for students’ learning of Artificial Intelligence Popular Science Activities. Frontiers in Psychology, 3156.
**e, H., Chu, H., Hwang, G., & Wang, C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140. https://doi.org/10.1016/j.compedu.2019.103599
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 1–20.
Yin, P., -., Chuang, K., & Hwang, G. (2016). Develo** a context-aware ubiquitous learning system based on a hyper-heuristic approach by taking real-world constraints into account. Universal Access in the Information Society, 15(3), 315–328. https://doi.org/10.1007/s10209-014-0390-z
Yueh, H., Lin, W., Wang, S., & Fu, L. (2020). Reading with robot and human companions in library literacy activities: A comparison study. British Journal of Educational Technology, 51(5), 1884–1900.
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), 1–27.
Zhai, X., He, P., & Krajcik, J. (2022a). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765–1794. https://doi.org/10.1002/tea.21773
Zou, D., Huang, X., Kohnke, L., Chen, X., Cheng, G., & **e, H. (2022). A bibliometric analysis of the trends and research topics of empirical research on TPACK. Education and Information Technologies, 27(8), 10585–10609.
Zulić, H. (2019). How AI can change/improve/influence music composition, performance and education: three case studies. INSAM Journal of Contemporary Music, Art and Technology, 1(2), 100–114.
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by FENGLIN JIA, DANER SUN and CHEEKIT LOOI. The first draft of the manuscript was written by FENGLIN JIA with supervisions by DANER SUN and CHEEKIT LOOI. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Jia, F., Sun, D. & Looi, Ck. Artificial Intelligence in Science Education (2013–2023): Research Trends in Ten Years. J Sci Educ Technol 33, 94–117 (2024). https://doi.org/10.1007/s10956-023-10077-6
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DOI: https://doi.org/10.1007/s10956-023-10077-6