KAJIAN SISTEMATIS: IMPLEMENTASI AI DALAM PENELITIAN PENDIDIKAN SAINS DI ERA PEMBELAJARAN DIGITAL
DOI:
https://doi.org/10.51878/edutech.v5i4.7855Keywords:
Kecerdasan Buatan, Pendidikan Sains, Media Pembelajaran DigitalAbstract
The rapid development of artificial intelligence (AI) in the digital learning era has driven significant transformations in science education research, particularly regarding how learning data is analyzed, how concept diagnosis is conducted, and how learning experiences are personalized. This study focuses on mapping the implementation of AI in science education research based on 25 relevant indexed international studies. Using a systematic review approach, articles were analyzed to identify trends, methodologies, contexts of use, and the impact of AI on science learning processes and outcomes. The study results indicate that AI technology in science education is most commonly applied in inquiry-based learning, adaptive learning systems, intelligent tutoring systems, learning analytics, physics misconception detection, automated feedback, augmented reality-based learning, and personalized learning experiences. AI has been shown to improve scientific thinking skills, the accuracy of misconception diagnosis, learning motivation, and the effectiveness of virtual experiments. Meta-findings from 25 sources indicate that the application of AI significantly contributes to improving students' conceptual understanding, scientific argumentation skills, and scientific inquiry performance. However, key challenges identified include teacher readiness, students' AI literacy, infrastructure availability, and the ethical use of learning data. This study concludes that integrating AI into science education research has significant potential to enhance learning quality, but requires sound policy, educator support, and instructional design for optimal and sustainable implementation.
ABSTRAK
Perkembangan pesat kecerdasan buatan (AI) dalam era pembelajaran digital telah mendorong transformasi signifikan dalam penelitian pendidikan sains, terutama terkait bagaimana data pembelajaran dianalisis, bagaimana diagnosis konsep dilakukan, serta bagaimana pengalaman belajar dipersonalisasi. Kajian ini berfokus pada pemetaan implementasi AI dalam penelitian pendidikan sains berdasarkan 25 studi internasional terindeks yang relevan. Melalui pendekatan kajian sistematis, artikel?artikel dianalisis untuk mengidentifikasi tren, metodologi, konteks penggunaan, serta dampak AI terhadap proses dan hasil belajar sains. Hasil kajian menunjukkan bahwa teknologi AI dalam pendidikan sains paling banyak diterapkan pada pembelajaran berbasis inkuiri, sistem pembelajaran adaptif, intelligent tutoring systems, analitik pembelajaran, deteksi miskonsepsi fisika, umpan balik otomatis, pembelajaran berbasis augmented reality, serta personalisasi pengalaman belajar. AI terbukti meningkatkan keterampilan berpikir ilmiah, akurasi diagnosis miskonsepsi, motivasi belajar, serta efektivitas eksperimen virtual. Meta-temuan dari 25 sumber menunjukkan bahwa penerapan AI memberikan kontribusi signifikan dalam meningkatkan pemahaman konsep, kemampuan argumentasi ilmiah, dan performa penyelidikan ilmiah siswa. Namun, tantangan utama yang ditemukan meliputi kesiapan guru, literasi AI siswa, ketersediaan infrastruktur, serta etika penggunaan data pembelajaran. Kajian ini menyimpulkan bahwa integrasi AI dalam penelitian pendidikan sains sangat potensial memperkuat kualitas pembelajaran, tetapi memerlukan kebijakan, dukungan pendidik, serta desain pembelajaran yang matang agar implementasinya optimal dan berkelanjutan.
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References
Arymbekov, B. (2023, May). Augmented reality application to support visualization of physics experiments. In 2023 IEEE International Conference on Smart Information Systems and Technologies (SIST) (pp. 52-55). https://doi.org/10.1109/SIST58284.2023.10223534
Barbosa, P. L. S., Carmo, R. A. F. D., Gomes, J. P., & Viana, W. (2024). Adaptive learning in computer science education: A scoping review. Education and Information Technologies, 29(8), 9139-9188. https://doi.org/10.1007/s10639-023-12066-z
Cojean, S., & Jamet, E. (2022). Does an interactive table of contents promote learning from videos? A study of consultation strategies and learning outcomes. British Journal of Educational Technology, 53(2), 269-285. https://doi.org/10.1111/bjet.13164
Edali, M., Sahem, Z., & Elkamel, A. (2024, October). Utilizing AI Chatbots to Enhance Students' Critical Thinking and Problem-Solving Skills in Numerical Methods to Promote Reproducibility. In Sebha University Conference Proceedings (Vol. 3, No. 2, pp. 83-89). https://doi.org/10.51984/sucp.v3i2.3189
Fadillah, M. A., Usmeldi, U., Lufri, L., Mawardi, M., & Festiyed, F. (2024). Exploring user perceptions: The impact of ChatGPT on high school students' physics understanding and learning. Advances in Mobile Learning Educational Research, 4(2), 1197-1207. https://doi.org/10.25082/AMLER.2024.02.013
Haudek, K. C., & Zhai, X. (2024). Examining the effect of assessment construct characteristics on machine learning scoring of scientific argumentation. International Journal of Artificial Intelligence in Education, 34(4), 1482-1509. https://doi.org/10.1007/s40593-023-00385-8
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
Hung, C. Y., Sun, J. C. Y., & Liu, J. Y. (2019). Effects of flipped classrooms integrated with MOOCs and game-based learning on the learning motivation and outcomes of students from different backgrounds. Interactive Learning Environments, 27(8), 1028-1046. https://doi.org/10.1080/10494820.2018.1481103
Hwang, G. J., Tang, K. Y., & Tu, Y. F. (2024). How artificial intelligence (AI) supports nursing education: profiling the roles, applications, and trends of AI in nursing education research (1993–2020). Interactive Learning Environments, 32(1), 373-392. https://doi.org/10.1080/10494820.2022.2086579
Jia, F., Sun, D., & Looi, C. K. (2024). Artificial intelligence in science education (2013–2023): Research trends in ten years. Journal of Science Education and Technology, 33(1), 94-117. https://doi.org/10.1007/s10956-023-10077-6
Kahaleh, R. (2025). Instructor Evaluation of Meta Smart Glasses: Assessing AI Accuracy, Explanation Quality, and Misconception Detection in Physics. The Physics Educator, 7, 2550006. https://doi.org/10.1142/S2661339525500064
Lai, C. J. (2025). Evaluating generative AI-assisted inquiry-based learning and metacognitive scaffolding for enhancing scientific vocabulary, content comprehension, and lab report writing. Interactive Learning Environments, 1-15. https://doi.org/10.1080/10494820.2025.2572786
Lai, C. L., Hwang, G. J., & Tu, Y. H. (2018). The effects of computer-supported self-regulation in science inquiry on learning outcomes, learning processes, and self-efficacy. Educational Technology Research and Development, 66(4), 863-892. https://doi.org/10.1007/s11423-018-9585-y
Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021, March). Developing middle school students' AI literacy. In Proceedings of the 52nd ACM technical symposium on computer science education (pp. 191-197). https://doi.org/10.1145/3408877.3432513
Long, Y., & Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27(1), 55-88. https://doi.org/10.1007/s11257-016-9186-6
Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. https://discovery.ucl.ac.uk/id/eprint/1475756/
Makransky, G., Andreasen, N. K., Baceviciute, S., & Mayer, R. E. (2021). Immersive virtual reality increases liking but not learning with a science simulation and generative learning strategies promote learning in immersive virtual reality. Journal of Educational Psychology, 113(4), 719. https://doi.org/10.1037/edu0000473
Peng, J., & Li, Y. (2025). Frontiers of Artificial Intelligence for Personalized Learning in Higher Education: A Systematic Review of Leading Articles. Applied Sciences, 15(18), 10096. https://doi.org/10.3390/app151810096
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0108-x
Sailer, M., Stadler, M., Schultz-Pernice, F., Franke, U., Schöffmann, C., Paniotova, V., ... & Fischer, F. (2021). Technology-related teaching skills and attitudes: Validation of a scenario-based self-assessment instrument for teachers. Computers in Human Behavior, 115, 106625. https://doi.org/10.1016/j.chb.2020.106625
Spector, J. M. (2017, November). Smart learning environments: Potential and pitfalls. In Educational Technology to Improve Quality and Access on a Global Scale: Papers from the Educational Technology World Conference (ETWC 2016) (pp. 33-42). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-66227-5_4
Su, Y. S., Lin, Y. D., & Liu, T. Q. (2022). Applying machine learning technologies to explore students’ learning features and performance prediction. Frontiers in Neuroscience, 16, 1018005. https://doi.org/10.3389/fnins.2022.1018005
Vincoli, M., Scholz, N., & Nagashima, T. (2025, July). Multidimensional student agency in learning with AI: a conceptual framework and design implications. In International Conference on Artificial Intelligence in Education (pp. 218-232). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-98459-4_16
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
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