Keywords:
Artificial Intelligence
Instructional Tools
Learning Environments
Science Education
Teaching Augmentor

  • Abstract

    The integration of Artificial Intelligence (AI) in science education has witnessed significant advancements, transforming traditional teaching and learning practices. This narrative review examines the roles of AI in science education over a decade (2013–2022), focusing on three key areas, namely, AI-based instructional tools that offer personalized and adaptive learning experiences; AI-enabled learning environments that facilitate immersive and interactive exploration of scientific concepts; and AI-supported assessment tools that enhance feedback and evaluation processes. The study highlights the potential of synergistically integrating these roles to create dynamic and adaptive educational systems. It also addresses challenges such as ethical considerations, teacher preparedness, and disparities in resources, which influence the adoption and efficacy of AI in science education. By exploring current trends, practices, and outcomes, this review underscores AI’s capacity to augment traditional teaching methods, fostering student engagement, critical thinking, and personalized learning pathways. Recommendations for teacher training and curriculum development are provided to ensure effective integration and sustainable implementation of AI technologies in science education.

  • References

    1. Ahmad, D., Latif, I., Arafah, B., & Suryadi, R. (2024). Defining the Role of Artificial Intelligence in Improving English Writing Skills Among Indonesian Students. Journal of Language Teaching and Research, 15(2), 568-678. https://doi.org/10.17507/jltr.1502.25
    2. Akkila, A. N., Almasri, A., Ahmed, A., Al-Masri, N., Abu Sultan, Y. S., Mahmoud, A. Y., Zaqout, I., & Abu-Naser, S. S. (2017). Survey of Intelligent Tutoring Systems up to the end of 2017. International Journal of Academic Information Systems Research, 3(4), 36 – 49.
    3. Aripin, I., Gaffar, A. A., Jabar, M. B. A., & Yulianti, D. (2024). Artificial intelligence in biology and learning biology: A literature review. Jurnal Mangifera Edu, 8(2), 41-48. https://doi.org/10.31943/mangiferaedu.v8i2.185
    4. Avramiotis, S., & Tsaparlis, G. (2013). Using Computer Simulations in Chemistry Problem-Solving. Chemistry Education Research and Practice, 14, 297–311. https://doi.org/10.1039/C3RP20167H
    5. Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., & Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Applied Sciences (Basel, Switzerland), 10(13), 4427. https://doi.org/10.3390/app10134427
    6. Ceberio, M., Almudí, J. M., & Franco, Á. (2016). Design and application of interactive simulations in problem-solving in university-level physics education. Journal of Science Education and Technology, 25(4), 590–609. https://doi.org/10.1007/s10956-016-9615-7
    7. Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial Intelligence (AI)-based tools into education. Computers in Human Behavior, 138(107468), 107468. https://doi.org/10.1016/j.chb.2022.107468
    8. Chang, S. L., & Ley, K. (2006). A learning strategy to compensate for cognitive overload in online learning: Learner use of printed online materials. Journal of Interactive Online Learning, 5(1), 104-117.
    9. Chans, G. M., & Portuguez Castro, M. (2021). Gamification as a strategy to increase motivation and engagement in higher education chemistry students. Computers, 10(10), 132. https://doi.org/10.3390/computers10100132
    10. Chou, C.-M., Shen, T.-C., Shen, T.-C., & Shen, C.-H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. Education and Information Technologies, 27(6), 8723–8750. https://doi.org/10.1007/s10639-021-10866-9
    11. Ciolacu, M., Tehrani, A. F., Binder, L., & Svasta, P. M. (2018). Education 4.0-Artificial Intelligence Assisted Higher Education: Early Recognition System with Machine Learning to Support Students’ Success. In 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging(SIITME) (pp. 23–30). IEEE. http://doi.org/10.1109/SIITME.2018.8599203
    12. Contreras, J. O., Hilles, S., & Abubakar, Z. B. (2019). Automated essay scoring using ontology generator and natural language processing with question generator based on Bloom’s taxonomy’s cognitive level. International Journal of Engineering and Advanced Technology, 2019(1), 2448–2457. http://doi.org/10.35940/ijeat.A9974.109119
    13. Cope, B., Kalantzis, M., & Searsmith, D. (2021). Artificial Intelligence for education: Knowledge and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory, 53(12), 1229–1245. https://doi.org/10.1080/00131857.2020.1728732
    14. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8
    15. Darvishi, A., Khosravi, H., Sadiq, S., & Gašević, D. (2022). Incorporating AI and Learning Analytics to Build Trustworthy Peer Assessment Systems. British Journal of Educational Technology, 53(4), 844–875. https://doi.org/10.1111/bjet.13233
    16. De Jong, T., Sotiriou, S., & Gillet, D. (2014). Innovations in STEM education: the Go-Lab federation of online labs. Smart Learning Environments, 1(1), 3. https://doi.org/10.1186/s40561-014-0003-6
    17. Delello, J. A. (2014). Insights from pre-service teachers using science-based augmented reality. Journal of Computers in Education, 1(4), 295–311. https://doi.org/10.1007/s40692-014-0021-y
    18. Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. https://doi.org/10.1016/j.chb.2015.05.041
    19. Hasanah, A., & Budiyono, S. (2024). Pemanfaatan Model Pembelajaran Futuristik Berbasis Artificial Intelligence (AI) dalam Dunia Pendidikan. Al-DYAS, 3(2), 615–625. https://doi.org/10.58578/aldyas.v3i2.2880
    20. Hsieh, M. C., & Chen, S. H. (2019). Intelligence augmented reality tutoring system for mathematics teaching and learning. Journal of Internet Technology, 20(5), 1673-1681. https://jit.ndhu.edu.tw/article/view/2148
    21. Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26(5), 5127–5147. https://doi.org/10.1007/s10639-021-10530-2
    22. Hwang, G.-J., & Chang, C.-Y. (2021). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments, 31(7), 1–14. https://doi.org/10.1080/10494820.2021.1952615
    23. Hwang, G.-J., Xie, 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), 100001. https://doi.org/10.1016/j.caeai.2020.100001
    24. Jagtap, P. (2016). Teachers role as facilitator in learning. Scholarly Research Journal, 3(17), 3903–3905.
    25. Jendia, J., & Ismail, H. H. (2023). Developing personalized reading materials for Malaysian primary school pupils using ChatGPT: A review. International Journal of Academic Research in Business and Social Sciences, 13(12). https://doi.org/10.6007/ijarbss/v13-i12/20172
    26. Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial Intelligence (AI) enabled e-learning. International Journal of Information and Learning Technology, 38(1), 1–19. https://doi.org/10.1108/ijilt-05-2020-0090
    27. Khan, M. A., & Maysoon Khojah, V. (2022). Artificial intelligence and big data: The advent of new pedagogy in the adaptive E-learning system in the higher educational institutions of Saudi Arabia. Education Research International, 2022, 1–10. https://doi.org/10.1155/2022/1263555
    28. Kiemde, S. M. A., & Kora, A. D. (2022). Towards an ethics of AI in Africa: rule of education. AI and Ethics, 2(1), 35–40. https://doi.org/10.1007/s43681-021-00106-8
    29. Lau, E. T., Sun, L., & Yang, Q. (2019). Modeling, prediction, and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9). https://doi.org/10.1007/s42452-019-0884-7
    30. Lau, K. H., Lam, T., Kam, B. H., Nkhoma, M., Richardson, J., & Thomas, S. (2018). The role of textbook learning resources in e-learning: A taxonomic study. Computers & Education, 118, 10–24. https://doi.org/10.1016/j.compedu.2017.11.005
    31. Luckin, R., & Holmes, W. (2016). Intelligence Unleashed: An Argument for AI in Education. London: Pearson.
    32. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123
    33. Madigan, D. J., Kim, L. E., Glandorf, H. L., & Kavanagh, O. (2023). Teacher burnout and physical health: A systematic review. International Journal of Educational Research, 119, 102173. https://doi.org/10.1016/j.ijer.2023.102173
    34. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-12. https://doi.org/10.1609/aimag.v27i4.1904
    35. Mnguni, L. (2023). A critical reflection on the affordances of Web 3.0 and artificial intelligence in life sciences education. Journal of Pedagogical Sociology and Psychology. https://doi.org/10.33902/jpsp.202322298
    36. Mnguni, L. (2024). A qualitative analysis of South African pre-service life sciences teachers' behavioral intentions for integrating AI in teaching. Journal for STEM Education Research. https://doi.org/10.1007/s41979-024-00128-x
    37. Mu, C. (2023). Research on talents cultivation model of application-oriented undergraduate in the era of AI. Proceedings of the 4th International Conference on Modern Education and Information Management, ICMEIM 2023, September 8-10, 2023, Wuhan, China. https://doi.org/10.4108/eai.8-9-2023.2339997
    38. Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the relationship between knowledge elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
    39. 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(1). https://doi.org/10.1186/s40561-023-00261-x
    40. Nkambou, R., Bourdeau, J., & Mizoguchi, R. (2010). Introduction: What are intelligent tutoring systems, and why this book? In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds). Advances in Intelligent Tutoring Systems. Studies in Computational Intelligence, 308, pp. 1-12 Springer. https://doi.org/10.1007/978-3-642-14363-2_1
    41. Opesemowo, O. A. G., & Adekomaya, V. (2024). Harnessing artificial intelligence for advancing Sustainable Development Goals in South Africa’s higher education system: A qualitative study. International Journal of Learning Teaching and Educational Research, 23(3), 67–86. https://doi.org/10.26803/ijlter.23.3.4
    42. Osasona, F., Daraojimba, A. I., Atadoga, A., Onwusinkwue, S., Obi, O. C., & Dawodu, S. O. (2024). Ai integration in Business Analytics: A review of USA and African trends. Computer Science & IT Research Journal, 5(2), 432–446. https://doi.org/10.51594/csitrj.v5i2.793
    43. Patole, S., Pawar, A., Patel, A., Panchal, A., & Joshi, R. (2016). Automatic system for grading multiple choice questions and feedback analysis. International Journal of Technical Research and Applications, 3(39), 16–19.
    44. Pharswan, H. (2022). Intelligent Tutoring in a VR Classroom. Rochester Institute of Technology. https://repository.rit.edu/cgi/viewcontent.cgi?article=12391&context=theses
    45. Ragheb, M. A., Tantawi, P., Farouk, N., & Hatata, A. (2022). Investigating the acceptance of applying chatbot (Artificial intelligence) technology among higher education students in Egypt. International Journal of Higher Education Management, 8(2). https://doi.org/10.24052/ijhem/v08n02/art-1
    46. Ruzek, E. A., Hafen, C. A., Allen, J. P., Gregory, A., Mikami, A. Y., & Pianta, R. C. (2016). How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learning and instruction, 42, 95-103. https://doi.org/10.1016/j.learninstruc.2016.01.004
    47. Selwyn, N. (2019). Should robots replace teachers?: AI and the future of education. John Wiley & Sons.
    48. Sethi, A., & Singh, K. (2022). Natural language processing-based automated essay scoring with parameter-efficient transformer approach. Proceedings of the Sixth International Conference on Computing Methodologies and Communication (ICCMC). http://doi.org/10.1109/ICCMC53470.2022.9753760
    49. Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3), 1209–1230. https://doi.org/10.1111/bjet.13425
    50. Tan, S., & Waugh, R. (2013). Use of virtual-reality in teaching and learning molecular biology. 3D immersive and interactive learning. In: Cai, Y. (eds) 3D Immersive and Interactive Learning. Springer, Singapore. https://doi.org/10.1007/978-981-4021-90-6_2
    51. Vozniuk, A., Rodríguez-Triana, M. J., Holzer, A., Govaerts, S., Sandoz, D., & Gillet, D. (2015, June). Contextual learning analytics apps to create awareness in blended inquiry learning. In 2015 International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1-5). IEEE. https://doi.org/10.1109/ITHET.2015.7218029
    52. Wannapiroon, N., & Pimdee, P. (2022). Thai Undergraduate Science, Technology, Engineering, Arts, and Math (STEAM) creative thinking and innovation skill development: a conceptual model using a digital virtual classroom learning environment. Education and Information Technologies, 27(4), 5689–5716. https://doi.org/10.1007/s10639-021-10849-w
    53. Wawan, C., Fenyvesi, K., Lathifah, A., & Ari, R. (2022). Computational thinking development: Benefiting from educational robotics in STEM teaching. European Journal of Educational Research, 11(4), 1997–2012. https://doi.org/10.12973/eu-jer.11.4.1997
    54. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
    55. Yang, H., Kim, J., & Lee, W. (2023). Analyzing the alignment between AI curriculum and AI textbooks through text mining. Applied Sciences, 13(18), 10011. https://doi.org/10.3390/app131810011
    56. 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(39), https://doi.org/10.1186/s41239-019-0171-0
    57. Zhu, M., Liu, O. L., & Lee, H. S. (2020). Using cluster analysis to explore students' interactions with automated feedback in an online Earth science task. International Journal of Quantitative Research in Education, 5(2), 111–135. https://doi.org/10.1504/IJQRE.2020.111452
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Mnguni, L. (2025). AI as a teaching augmentor: A review of the integration of AI in science education. Multidisciplinary Reviews, (| Accepted Articles). Retrieved from https://malque.pub/ojs/index.php/mr/article/view/8361
  • Article viewed - 77