Review Article

Artificial intelligence in science education: A systematic review of applications, impacts, and challenges

Albinа R. Fayzullina 1 * , Alla A. Filippova 2 , Natalya Y. Garnova 2 , Dmitry V. Astakhov 2 , Nadezhda Kalmazova 3 , Zulfiya F. Zaripova 4
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1 Kazan (Volga Region) Federal University, Kazan, RUSSIA2 I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, RUSSIA3 Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University), Moscow, RUSSIA4 Almetyevsk State Technological University «Petroleum High School», Almetyevsk, RUSSIA* Corresponding Author
Contemporary Educational Technology, 17(4), October 2025, ep613, https://doi.org/10.30935/cedtech/17519
Published: 06 December 2025
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ABSTRACT

This systematic review investigates the incorporation of artificial intelligence (AI) into science education by analyzing 17 studies published from 2020 to 2024. The paper examines the utilization of AI in different scientific fields and educational settings and assesses its influence on the methods of teaching and learning. The findings demonstrate a diverse range of AI applications, including chatbots, intelligent tutoring systems, and AI-enhanced textbooks. These apps serve many functions, from being educational tools to assisting in assessments. The investigation demonstrates the favorable impact of AI on student performance, motivation, and engagement in science education, particularly in the areas of personalized learning and the development of self-regulated learning skills. Additionally, issues related to technological infrastructure, obstacles to the sensitivity and reliability of AI systems, and ethical issues were also examined. The study emphasizes the importance of teacher preparation in achieving the successful integration of AI and expresses the necessity of comprehensive professional development. Potential areas for future research encompass investigating the enduring consequences of AI utilization, exploring its applicability in diverse educational settings, and fostering the growth of AI literacy. The study’s findings indicate that while AI has the potential to greatly improve science education, its successful application necessitates thoughtful evaluation of technological, pedagogical, ethical, and social elements to ensure fair and efficient integration across all educational levels.

CITATION (APA)

Fayzullina, A. R., Filippova, A. A., Garnova, N. Y., Astakhov, D. V., Kalmazova, N., & Zaripova, Z. F. (2025). Artificial intelligence in science education: A systematic review of applications, impacts, and challenges. Contemporary Educational Technology, 17(4), ep613. https://doi.org/10.30935/cedtech/17519

REFERENCES

  1. Adelana, O. P., Ayanwale, M. A., & Sanusi, I. T. (2024). Exploring pre-service biology teachers’ intention to teach genetics using an AI intelligent tutoring-based system. Cogent Education, 11(1), Article 2310976. https://doi.org/10.1080/2331186X.2024.2310976
  2. Ahmad, S. F., Alam, M. M., Rahmat, M. K., Mubarik, M. S., & Hyder, S. I. (2022). Academic and administrative role of artificial intelligence in education. Sustainability, 14(3), Article 1101. https://doi.org/10.3390/su14031101
  3. Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, Article 100132. https://doi.org/10.1016/j.caeai.2023.100132
  4. Allen, L. K., Creer, S. C., & Öncel, P. (2022). Natural language processing: Towards a multi-dimensional view of the learning process. In C. Lang, G. Siemens, A. F. Wise, D. Gašević, & A. Merceron (Eds.), The handbook of learning analytics (pp. 46-53). SoLAR. https://doi.org/10.18608/hla22.005
  5. Almasri, F. (2024). Exploring the impact of artificial intelligence in teaching and learning of science: A systematic review of empirical research. Research in Science Education, 54, 977-997. https://doi.org/10.1007/s11165-024-10176-3
  6. Aloisi, C. (2023). The future of standardised assessment: Validity and trust in algorithms for assessment and scoring. European Journal of Education, 58(1), 98-110. https://doi.org/10.1111/ejed.12542
  7. AlQuraishi, M., & Sorger, P. K. (2021). Differentiable biology: Using deep learning for biophysics-based and data-driven modeling of molecular mechanisms. Nature Methods, 18(10), 1169-1180. https://doi.org/10.1038/s41592-021-01283-4
  8. Alshorman, S. (2024). The readiness to use AI in teaching science: Science teachers’ perspective. Journal of Baltic Science Education, 23(3), 432-448. https://doi.org/10.33225/jbse/24.23.432
  9. Baum, Z. J., Yu, X., Ayala, P. Y., Zhao, Y., Watkins, S. P., & Zhou, Q. (2021). Artificial intelligence in chemistry: Current trends and future directions. Journal of Chemical Information and Modeling, 61(7), 3197-3212. https://doi.org/10.1021/acs.jcim.1c00619
  10. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa
  11. Brown, C. E., Alrmuny, D., Williams, M. K., Whaley, B., & Hyslop, R. M. (2021). Visualizing molecular structures and shapes: A comparison of virtual reality, computer simulation, and traditional modeling. Chemistry Teacher International, 3(1), 69-80. https://doi.org/10.1515/cti-2019-0009
  12. Chang, J., Park, J., & Park, J. (2023). Using an artificial intelligence chatbot in scientific inquiry: Focusing on a guided-inquiry activity using inquirybot. Asia-Pacific Science Education, 2(2), 1-31. https://doi.org/10.1163/23641177-bja10062
  13. 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, 18621-18642. https://doi.org/10.1007/s10639-024-12553-x
  14. Chen, P. Y., & Liu, Y. C. (2024). Impact of AI robot image recognition technology on improving students’ conceptual understanding of cell division and science learning motivation. Journal of Baltic Science Education, 23(2), 208-220. https://doi.org/10.33225/jbse/24.23.208
  15. Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182. https://doi.org/10.1007/s10796-022-10291-4
  16. Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments, 32(7), 3240-3256. https://doi.org/10.1080/10494820.2023.2172044
  17. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452. https://doi.org/10.1007/s10956-023-10039-y
  18. Cooper, G., & Tang, K. S. (2024). Pixels and pedagogy: Examining science education imagery by generative artificial intelligence. Journal of Science Education and Technology, 33(4), 556-568. https://doi.org/10.1007/s10956-024-10104-0
  19. Dai, C. P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, Article 100087. https://doi.org/10.1016/j.caeai.2022.100087
  20. Dai, Y. (2023). Negotiation of epistemological understandings and teaching practices between primary teachers and scientists about artificial intelligence in professional development. Research in Science Education, 53(3), 577-591. https://doi.org/10.1007/s11165-022-10072-8
  21. 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
  22. Ercikan, K., & McCaffrey, D. F. (2022). Optimizing implementation of artificial-intelligence-based automated scoring: An evidence centered design approach for designing assessments for AI-based scoring. Journal of Educational Measurement, 59(3), 272-287. https://doi.org/10.1111/jedm.12332
  23. Erduran, S., & Levrini, O. (2024). The impact of artificial intelligence on scientific practices: An emergent area of research for science education. International Journal of Science Education, 46(8), 1982-1989. https://doi.org/10.1080/09500693.2024.2306604
  24. Ezquerra, A., Agen, F., Rodríguez-Arteche, I., & Ezquerra-Romano, I. (2022). Integrating artificial intelligence into research on emotions and behaviors in science education. Eurasia Journal of Mathematics, Science and Technology Education, 18(4), Article em2099. https://doi.org/10.29333/EJMSTE/11927
  25. Felix, C. V. (2020). The role of the teacher and AI in education. In E. Sengupta, P. Blessinger, & M. Makhanya (Eds.), International perspectives on the role of technology in humanizing higher education (pp. 33-48). Emerald Publishing. https://doi.org/10.1108/s2055-364120200000033003
  26. Garofalo, S. G., & Farenga, S. J. (2024). Science teacher perceptions of the state of knowledge and education at the advent of generative artificial intelligence popularity. Science & Education, 34, 893-912. https://doi.org/10.1007/s11191-024-00534-y
  27. González-Calatayud, V., Prendes-Espinosa, P., Roig-Vila, R., & Carpanzano, E. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467
  28. Gough, D., Oliver, S., & Thomas, J. (2012). An introduction to systemic reviews. SAGE.
  29. Greenfield, D., Zan, B., McWayne, C., Harris, M., Alexander, A., Ochoa, W., & Mistry, J. (2024). Early childhood science practices observation tool (EC-SPOT): Assessing science practices across multiple classroom contexts. International Journal of Science Education, 46(18), 1963-1981. https://doi.org/10.1080/09500693.2024.2305634
  30. Gulson, K. N., & Witzenberger, K. (2022). Repackaging authority: Artificial intelligence, automated governance and education trade shows. Journal of Education Policy, 37(1), 145-160. https://doi.org/10.1080/02680939.2020.1785552
  31. Gunawan, K. D. H., Liliasari, Kaniawati, I., Setiawan, W., Rochintaniawati, D., & Sinaga, P. (2021). Profile of teachers’ integrated science curricula that support by intelligent tutoring systems. Journal of Physics: Conference Series, 1806, Article 012139. https://doi.org/10.1088/1742-6596/1806/1/012139
  32. Heeg, D. M., & Avraamidou, L. (2023). The use of artificial intelligence in school science: A systematic literature review. Educational Media International, 60(2), 125-150. https://doi.org/10.1080/09523987.2023.2264990
  33. Kaviyaraj, R., & Uma, M. (2021). A survey on future of augmented reality with AI in education. In Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (pp. 47-52). IEEE. https://doi.org/10.1109/ICAIS50930.2021.9395838
  34. Kim, M. C., Zhu, Y., & Chen, C. (2016). How are they different? A quantitative domain comparison of information visualization and data visualization (2000-2014). Scientometrics, 107(1), 123-165. https://doi.org/10.1007/s11192-015-1830-0
  35. Koć-Januchta, M. M., Schönborn, K. J., Tibell, L. A. E., Chaudhri, V. K., & Heller, H. C. (2020). Engaging with biology by asking questions: Investigating students’ interaction and learning with an artificial intelligence-enriched textbook. Journal of Educational Computing Research, 58(6), 1190-1224. https://doi.org/10.1177/0735633120921581
  36. Krenn, M., Pollice, R., Guo, S. Y., Aldeghi, M., Cervera-Lierta, A., Friederich, P., dos Passos Gomes, G., Häse, F., Jinich, A., Nigam, A. K., Yao, Z., & Aspuru-Guzik, A. (2022). On scientific understanding with artificial intelligence. Nature Reviews Physics, 4(12), 761-769. https://doi.org/10.1038/s42254-022-00518-3
  37. Krippendorff, K. (2018). Content analysis: An introduction to its methodology. SAGE. https://doi.org/10.4135/9781071878781
  38. Kruit, P. M., Bredeweg, B., & Nieuwelink, H. (2024). Addressing socio-scientific issues with interactive concept cartoons: Design of a web-based educational instrument. International Journal of Science Education, 47(7), 870-890. https://doi.org/10.1080/09500693.2024.2354076
  39. Kurniawan, W., Riantoni, C., Lestari, N., & Ropawandi, D. (2024). A hybrid automatic scoring system: Artificial intelligence-based evaluation of physics concept comprehension essay test. International Journal of Information and Education Technology, 14(6), 876-882. https://doi.org/10.18178/ijiet.2024.14.6.2113
  40. Lee, D., Kim, H. hyeon, & Sung, S. H. (2023a). Development research on an AI English learning support system to facilitate learner-generated-context-based learning. Educational Technology Research and Development, 71(2), 629-666. https://doi.org/10.1007/s11423-022-10172-2
  41. Lee, G. G., & Zhai, X. (2024). Using ChatGPT for science learning: A study on pre-service teachers’ lesson planning. IEEE Transactions on Learning Technologies, 17, 1683-1700. https://doi.org/10.1109/TLT.2024.3401457
  42. Lee, G. G., Choi, M., An, T., Mun, S., & Hong, H. G. (2023b). Development of the hands-free AI speaker system supporting hands-on science laboratory class: A rapid prototyping. International Journal of Emerging Technologies in Learning, 18(1), 115-136. https://doi.org/10.3991/ijet.v18i01.34843
  43. Lee, G. G., Mun, S., Shin, M. K., & Zhai, X. (2024). Collaborative learning with artificial intelligence speakers: Pre-service elementary science teachers’ responses to the prototype. Science & Education, 34, 847-875. https://doi.org/10.1007/s11191-024-00526-y
  44. Lee, J., An, T., Chu, H. E., Hong, H. G., & Martin, S. N. (2023c). Improving science conceptual understanding and attitudes in elementary science classes through the development and application of a rule-based AI chatbot. Asia-Pacific Science Education, 13(2). https://doi.org/10.1163/23641177-bja10070
  45. Liao, X., Zhang, X., Wang, Z., & Luo, H. (2024). Design and implementation of an AI-enabled visual report tool as formative assessment to promote learning achievement and self-regulated learning: An experimental study. British Journal of Educational Technology, 55(3), 1253-1276. https://doi.org/10.1111/bjet.13424
  46. Lin, Y. T., & Ye, J. H. (2023). Development of an educational chatbot system for enhancing students’ biology learning performance. Journal of Internet Technology, 24(2), 275-281. https://doi.org/10.53106/160792642023032402006
  47. Matovu, H., Ungu, D. A. K., Won, M., Tsai, C. C., Treagust, D. F., Mocerino, M., & Tasker, R. (2023). Immersive virtual reality for science learning: Design, implementation, and evaluation. Studies in Science Education, 59(2), 205-244. https://doi.org/10.1080/03057267.2022.2082680
  48. Mikeska, J. N., & Lottero-Perdue, P. S. (2022). How preservice and in-service elementary teachers engage student avatars in scientific argumentation within a simulated classroom environment. Science Education, 106(4), 980-1009. https://doi.org/10.1002/sce.21726
  49. Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, Article 100050. https://doi.org/10.1016/j.caeai.2022.100050
  50. 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, Article 1. https://doi.org/10.1186/2046-4053-4-1
  51. Naya-Forcano, A., Garcia-Bosque, M., Cascarosa, E., Aznar, F., Sánchez-Azqueta, C., Celma, S., & Aldea, C. (2024, June 27). ChatPLT: An intelligent tutoring system for teaching physics in higher education. In Proceedings of the 10th International Conference on Higher Education Advances (pp. 978-985). https://doi.org/10.4995/head24.2024.17261
  52. 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
  53. Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 49(5), 847-864. https://doi.org/10.1080/03075079.2024.2323593
  54. Nja, C. O., Idiege, K. J., Uwe, U. E., Meremikwu, A. N., Ekon, E. E., Erim, C. M., Ukah, J. U., Eyo, E. O., Anari, M. I., & Cornelius-Ukpepi, B. U. (2023). Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 10, Article 42. https://doi.org/10.1186/s40561-023-00261-x
  55. Nurshatayeva, A., Page, L. C., White, C. C., & Gehlbach, H. (2021). Are artificially intelligent conversational chatbots uniformly effective in reducing summer melt? Evidence from a randomized controlled trial. Research in Higher Education, 62(3), 392-402. https://doi.org/10.1007/s11162-021-09633-z
  56. Nykonenko, A. (2023). The impact of artificial intelligence on modern education: Prospects and challenges. Artificial Intelligence, 28(AI.2023.28(2)), 10-15. https://doi.org/10.15407/jai2023.02.010
  57. Osborne, J., & Allchin, D. (2024). Science literacy in the twenty-first century: Informed trust and the competent outsider. International Journal of Science Education, 47(15-16), 2134-2155. https://doi.org/10.1080/09500693.2024.2331980
  58. Ottander, K., & Simon, S. (2021). Learning democratic participation? Meaning-making in discussion of socioscientific issues in science education. International Journal of Science Education, 43(12), 1895-1925. https://doi.org/10.1080/09500693.2021.1946200
  59. Park, J., Teo, T. W., Teo, A., Chang, J., Huang, J. S., & Koo, S. (2023). Integrating artificial intelligence into science lessons: Teachers’ experiences and views. International Journal of STEM Education, 10, Article 61. https://doi.org/10.1186/s40594-023-00454-3
  60. Patero, J. L. (2023). AI in the classroom: Transforming physics instruction for the digital age. International Journal of Advanced Research in Science, Communication and Technology, 3(1), 801-806. https://doi.org/10.48175/ijarsct-12374
  61. Peters-Burton, E., Rich, P. J., Kitsantas, A., Laclede, L., & Stehle, S. M. (2022). High school science teacher use of planning tools to integrate computational thinking. Journal of Science Teacher Education, 33(6), 598-620. https://doi.org/10.1080/1046560X.2021.1970088
  62. Petticrew, M., & Roberts, H. (2008). Systematic reviews in the social sciences: A practical guide. John Wiley & Sons.
  63. Ramkorun, B. (2024). Graph plotting of 1-D motion in introductory physics education using scripts generated by ChatGPT 3.5. Physics Education, 59(2), Article 025020. https://doi.org/10.1088/1361-6552/ad2191
  64. Rojas, A. J. (2024). An investigation into ChatGPT’s application for a scientific writing assignment. Journal of Chemical Education, 101(5), 1959-1965. https://doi.org/10.1021/acs.jchemed.4c00034
  65. Román, D., del Rosal, K., & Basaraba, D. (2019). Constructing explanations in science: Informal formative assessment practices among science teachers of English learners. Research in Science Education, 49(4), 1055-1067. https://doi.org/10.1007/s11165-019-9849-5
  66. Rotatori, D., Lee, E. J., & Sleeva, S. (2021). The evolution of the workforce during the fourth industrial revolution. Human Resource Development International, 24(1), 92-103. https://doi.org/10.1080/13678868.2020.1767453
  67. Sadler, T. D., Mensah, F. M., & Tam, J. (2024). Artificial intelligence and the Journal of Research in Science Teaching. Journal of Research in Science Teaching, 61(4), 739-743. https://doi.org/10.1002/tea.21933
  68. Sarrab, M., Pulparambil, S., & Awadalla, M. (2020). Development of an IoT based real-time traffic monitoring system for city governance. Global Transitions, 2, 230-245. https://doi.org/10.1016/j.glt.2020.09.004
  69. Selvam, A. A. A. (2024). Exploring the impact of artificial intelligence on transforming physics, chemistry, and biology education. The Cuvette, 2. https://doi.org/10.21428/a70c814c.747297aa
  70. Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner-instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18, Article 54. https://doi.org/10.1186/s41239-021-00292-9
  71. Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: Assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1260843
  72. Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, Article 100065. https://doi.org/10.1016/j.caeai.2022.100065
  73. Su, K.-D. (2022). Implementation of innovative artificial intelligence cognitions with problem-based learning guided tasks to enhance students’ performance in science. Journal of Baltic Science Education, 21(2), 245-257. https://doi.org/10.33225/jbse/22.21.245
  74. Sun, H., Xie, Y., & Lavonen, J. (2022). Exploring the structure of students’ scientific higher order thinking in science education. Thinking Skills and Creativity, 43, Article 100999. https://doi.org/10.1016/j.tsc.2022.100999
  75. Taani, O., & Alabidi, S. (2024). ChatGPT in education: Benefits and challenges of ChatGPT for mathematics and science teaching practices. International Journal of Mathematical Education in Science and Technology, 56(9), 1748-1777. https://doi.org/10.1080/0020739X.2024.2357341
  76. Tassoti, S. (2024). Assessment of students use of generative artificial intelligence: Prompting strategies and prompt engineering in chemistry education. Journal of Chemical Education, 101(6), 2475-2482. https://doi.org/10.1021/acs.jchemed.4c00212
  77. Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356-367. https://doi.org/10.1177/1534484305278283
  78. Walan, S. (2020). Embracing digital technology in science classrooms–secondary school teachers’ enacted teaching and reflections on practice. Journal of Science Education and Technology, 29(3), 431-441. https://doi.org/10.1007/s10956-020-09828-6
  79. Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., Anandkumar, A., Bergen, K., Gomes, C. P., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T. Y., Manrai, A., … Zitnik, M. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620(7972), 47-60. https://doi.org/10.1038/s41586-023-06221-2
  80. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii-xxiii.
  81. Wulandari, F., & Hadi, S. (2021). Development of computerized adaptive testing to measure students’ logical thinking skills in science learning. Psychology and Education, 58(2), 4465-4474.
  82. 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, Article 59. https://doi.org/10.1186/s40594-022-00377-5
  83. Yaki, A. A. (2022). Fostering critical thinking skills using integrated stem approach among secondary school biology students. European Journal of STEM Education, 7(1), Article 06. https://doi.org/10.20897/ejsteme/12481
  84. Yang, Q. F., Lian, L. W., & Zhao, J. H. (2023). Developing a gamified artificial intelligence educational robot to promote learning effectiveness and behavior in laboratory safety courses for undergraduate students. International Journal of Educational Technology in Higher Education, 20, Article 18. https://doi.org/10.1186/s41239-023-00391-9
  85. Yannier, N., Hudson, S. E., & Koedinger, K. R. (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
  86. Young, J. D., Dawood, L., & Lewis, S. E. (2024). Chemistry students’ artificial intelligence literacy through their critical reflections of chatbot responses. Journal of Chemical Education, 101(6), 2466-2474. https://doi.org/10.1021/acs.jchemed.4c00154
  87. Zidny, R., Sjöström, J., & Eilks, I. (2020). A multi-perspective reflection on how indigenous knowledge and related ideas can improve science education for sustainability. Science & Education, 29(1), 145-185. https://doi.org/10.1007/s11191-019-00100-x