Integrasi AI Tutor dengan Simulasi Interaktif PHET untuk Meningkatkan Pemahaman Konsep Gelombang di Sekolah Menengah Atas
DOI:
https://doi.org/10.54065/pelita.6.1.2026.1008Keywords:
AI Tutor, Simulasi PhET, Pemahaman Konsep GelombangAbstract
Urgensi penelitian ini yaitu bahwa Integrasi AI Tutor dengan simulasi interaktif PhET menjadi mendesak karena pembelajaran konsep gelombang yang abstrak di SMA masih sulit dipahami siswa tanpa pendampingan adaptif dan visualisasi dinamis yang interaktif. Penelitian ini bertujuan mengevaluasi efektivitas integrasi AI Tutor dengan simulasi interaktif PhET dalam meningkatkan pemahaman konsep optik peserta didik di SMA Negeri 4 Luwu. Metode penelitian menggunakan pendekatan Research and Development (R&D) model Borg & Gall yang dimodifikasi, meliputi analisis kebutuhan, pengembangan produk, validasi ahli, uji coba terbatas, serta implementasi lapangan. Desain kuasi-eksperimental digunakan untuk membandingkan hasil belajar antara kelompok eksperimen yang menggunakan AI Tutor–PhET dan kelompok kontrol yang hanya menggunakan simulasi PhET. Sebanyak 60 peserta didik kelas XI terlibat sebagai sampel penelitian. Instrumen yang digunakan meliputi tes pemahaman konsep, angket respons, dan lembar observasi. Hasil penelitian menunjukkan adanya peningkatan signifikan pemahaman konsep optik pada kelompok eksperimen dibandingkan kelompok kontrol (p < 0,05). Nilai normalized gain kelompok eksperimen berada pada kategori sedang (g = 0,60), sedangkan kelompok kontrol berada pada kategori rendah. Selain itu, 87% peserta didik menyatakan AI Tutor memberikan umpan balik yang membantu dan meningkatkan motivasi belajar. Integrasi AI Tutor–PhET terbukti efektif dalam memperkuat pemahaman konsep optik, meningkatkan keterlibatan belajar, serta mendukung pembelajaran abad ke-21 yang adaptif dan berbasis teknologi.
References
Aisyah, N., Erwing, E., & Muliana, M. (2025). Implementasi Inovasi Teknologi Berbasis Augmented Reality terhadap Peningkatan Kemampuan Pemahaman Konsep Biologi Siswa SMA. Jurnal Pelita: Jurnal Pembelajaran IPA Terpadu, 5(1), 237–248. https://doi.org/10.54065/pelita.5.1.2025.610
Alsalhi, N. R., Abdelkader, A. F., Ismail, A. A. K. H., Alqatawneh, S., Alqawasmi, A., & Salem, O. (2024). The Effect of Using PhET Interactive Simulations on Academic Achievement of Physics Students in Higher Education Institutions. Educational Sciences: Theory & Practice, 24(1). https://doi.org/10.1108/JRIT-12-2021-0152
Banda, H. J., & Nzabahimana, S. (2022). The impact of physics education technology (PhET) simulations on students’ understanding of oscillations and waves. Physics Education, 57(6), 065010. https://doi.org/10.1088/1361-6552/ac8b2b
Ubben, M., & Bitzenbauer, P. (2023). Exploring the relationship between students’ conceptual understanding and model thinking in quantum optics. Frontiers in Quantum Science and Technology, 2, 1207619. https://doi.org/10.3389/frqst.2023.1207619
Farhana, S., et al. (2024). SimPal: An LLM-based pedagogical agent for adaptive physics tutoring. Computers & Education, 190, 104557. https://doi.org/10.48550/arXiv.2407.06241
Furió, D., Juan, M. C., Seguí, I., & Vivó, R. (2020). Mobile learning vs. traditional classroom lessons: A comparative study. Journal of Computer Assisted Learning, 36(3), 327–340. https://doi.org/10.1111/jcal.12403
Graesser, A. C., Conley, M. W., & Olney, A. (2021). Intelligent tutoring systems. In R. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed., pp. 395–415). New York, NY: Routledge. https://doi.org/10.4324/9781315450514
Handriyani, E., & Abdillah, C. (2022). Implementasi Model Kooperatif Two Stay Two Stray (TSTS) Untuk Meningkatkan Kemampuan Kognitif IPA. Jurnal Pelita: Jurnal Pembelajaran IPA Terpadu, 2(2), 69–75. https://doi.org/10.54065/pelita.2.2.2022.203
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign. https://doi.org/10.4324/9781003156079
Kusuma, A. S., Hidayat, R., & Prasetyo, B. (2021). Effect of interactive simulations on physics learning outcomes. Journal of Physics Education Research, 9(2), 120–131.
Létourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning, 10(1), 29. https://doi.org/10.1038/s41539-025-00320-7
Martin, F., Zhuang, M., & Schaefer, D. (2024). Systematic review of research on artificial intelligence in K-12 education (2017–2022). Computers and Education: Artificial Intelligence, 6, 100195. https://doi.org/10.1016/j.caeai.2023.100195
Nasir, M., & Wijaya, A. (2021). Challenges in learning optics in Indonesian high schools. Journal of Science Learning, 4(1), 45–54.
Otero, M. R., & Arlego, M. F. (2023). Teaching and learning optics in high school: From Fermat to Feynman. Physics, 4(4), 1117–1134. https://doi.org/10.3390/educsci13050503?utm_source=chatgpt.com
Pranata, O. D. (2024). Physics education technology (PhET) as a game-based learning tool: A quasi-experimental study. Pedagogical Research, 9(4), em0221. https://doi.org/10.29333/pr/15154
Putri, Y., & Farhana, H. (2025). Strategi Diferensiasi Produk berbantuan Media Audio Visual untuk Meningkatkan Pemahaman pada Siswa Sekolah Dasar. Jurnal Pelita: Jurnal Pembelajaran IPA Terpadu, 5(1), 214–224. https://doi.org/10.54065/pelita.5.1.2025.822
Sari, D., Putra, A., & Mulyani, T. (2020). PhET simulation in improving conceptual understanding. Jurnal Pendidikan Fisika Indonesia, 16(1), 55–64. https://doi.org/10.33394/jp.v10i3.7879
Sebald, J., Fliegauf, K., Veith, J. M., Spiecker, H., & Bitzenbauer, P. (2022). The world through my eyes: Fostering students’ understanding of basic optics concepts related to vision and image formation. Physics, 4(4), 1117-1134. https://doi.org/10.3390/physics4040073
Thomas, K., et al. (2023). The role of AI tutors in supporting personalized learning in science education. International Journal of STEM Education, 10(5), 1–17.
Vorobyeva, K. I., Belous, S., Savchenko, N. V., Smirnova, L. M., Nikitina, S. A., & Zhdanov, S. P. (2025). Personalized Learning through AI: Pedagogical Approaches and Critical Insights. Contemporary Educational Technology, 17(2). https://doi.org/10.30935/cedtech/16108
Wang, S. (2024). Artificial intelligence in education: A systematic literature review. Computers in Human Behavior, 137, 107415. https://doi.org/10.1016/j.eswa.2024.124167
Zhai, X., He, P., & Krajcik, J. (2023). Applying artificial intelligence in science education to promote personalized learning. Journal of Research in Science Teaching, 60(5), 720–752. https://doi.org/10.1002/tea.21799
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