MODEL PEMBELAJARAN ADAPTIF BERBASIS KECERDASAN BUATAN: PELUANG DAN TANTANGAN DALAM MEWUJUDKAN PENDIDIKAN PERSONALISASI

Authors

  • Supriyatmoko Supriyatmoko STIT Bustanul ‘Ulum Lampung Tengah
  • Khoirul Anam STIT Bustanul ‘Ulum Lampung Tengah
  • Wakib Kurniawan STIT Bustanul ‘Ulum Lampung Tengah

DOI:

https://doi.org/10.51878/strategi.v5i1.4944

Keywords:

Pembelajaran Adaptif, Kecerdasan Buatan, Pendidikan Personalisasi, Teknologi Pendidikan, AI dalam Pendidikan

Abstract

ABSTRACT

The rapid development of information and communication technology has opened up great opportunities for educational transformation, one of which is through the implementation of an adaptive learning model based on artificial intelligence (AI). This model aims to realise personalised education by adjusting materials, learning pace, and teaching strategies according to the needs and characteristics of each student. This research uses a qualitative approach with a systematic literature study that reviews more than 50 scientific publications and reports related to the implementation of AI in adaptive education. The research stages included identification of global trends, analysis of commonly used adaptive models, and evaluation of challenges and opportunities in various educational contexts. The results show that AI-based adaptive learning can significantly improve students' motivation, active participation, and learning outcomes, especially when the system is able to adjust interventions in real-time. However, the main challenges faced include limited digital infrastructure, potential algorithm bias, and the need for teacher training in the utilisation of the technology. The conclusion of this study emphasises that the successful implementation of AI-based adaptive learning models is highly dependent on the synergy between technology, education policy and school ecosystem readiness. A cross-sector collaborative strategy is needed to make AI a tool that supports equitable education equity and quality.

ABSTRAK

Perkembangan teknologi informasi dan komunikasi yang pesat telah membuka peluang besar bagi transformasi pendidikan, khususnya melalui penerapan model pembelajaran adaptif berbasis kecerdasan buatan (AI). Model ini bertujuan untuk mewujudkan pendidikan yang terpersonalisasi dengan menyesuaikan materi, kecepatan belajar, dan strategi pengajaran sesuai kebutuhan serta karakteristik masing-masing siswa. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi literatur sistematis, yang menganalisis lebih dari 50 publikasi ilmiah dan laporan kebijakan pendidikan global. Tahapan penelitian meliputi identifikasi tren dan perkembangan AI dalam pendidikan, klasifikasi model pembelajaran adaptif yang banyak diterapkan, serta evaluasi tantangan dan peluang implementasinya di berbagai konteks pendidikan, baik di negara maju maupun berkembang. Hasil analisis menunjukkan bahwa pembelajaran adaptif berbasis AI secara signifikan dapat meningkatkan motivasi, partisipasi aktif, dan capaian akademik siswa, terutama saat sistem mampu memberikan intervensi pembelajaran secara real-time dan berbasis data. Namun, beberapa tantangan yang muncul antara lain adalah keterbatasan infrastruktur digital, potensi bias dalam algoritma AI, serta kurangnya literasi teknologi pada tenaga pendidik. Studi ini menyimpulkan bahwa keberhasilan implementasi AI dalam pendidikan adaptif sangat bergantung pada kesiapan teknologi, dukungan kebijakan yang inklusif, serta kolaborasi antara sektor pendidikan, teknologi, dan pemerintah. Pendekatan holistik dan kolaboratif diperlukan agar AI dapat berkontribusi nyata dalam mewujudkan pendidikan yang merata, responsif, dan berkeadilan.

References

Baker, R. S., Heffernan, N. T., & Heffernan, C. L. (2020). The role of artificial intelligence in education: A review of research and applications. Journal of Educational Technology Systems, 48(3), 274-294.

Bakhtiar, M., & Herdiansyah, H. (2022). Adaptive learning systems in Indonesian education: Opportunities and challenges. Indonesian Journal of Educational Technology, 1(2), 55–68.

Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia computer science, 136, 16-24.

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Ferreira, J., García-Peñalvo, F. J., & Rodrigues, H. (2021). A framework for ethical issues in learning analytics in higher education. Education and Information Technologies, 26(2), 2257–2274.

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., ... & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of research in education, 44(1), 130-160.

Heffernan, N. T., & Heffernan, C. L. (2020). The impact of adaptive learning technologies on student performance and engagement: A review of recent research. Educational Research Review, 28, 100295.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Holstein, K., McLaren, B. M., & Aleven, V. (2020). Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–14). https://doi.org/10.1145/3313831.3376723

Johnson, L., Adams Becker, S., & Cummins, M. (2022). The NMC Horizon Report: 2022 Higher Education Edition. The New Media Consortium.

Kizilcec, R. F., Bailenson, J. N., & Pritchard, D. (2023). Adaptive learning technology: Can AI meet the needs of diverse students?. Journal of Educational Psychology, 115(2), 142-156.

Kuo, Y.-F., Chen, C.-Y., & Chang, C.-C. (2021). Exploring the impact of AI-driven adaptive learning on student engagement and achievement in online learning environments. Computers & Education, 164, 104-113.

Khosravi, H., Kitto, K., & Buckingham Shum, S. (2021). Personalized learning analytics: A review of the literature. British Journal of Educational Technology, 52(1), 165–190.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2020). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.

Lu, J., Xu, B., & Li, C. (2023). Adaptive learning systems in practice: Benefits, challenges, and design strategies. Educational Technology Research and Development, 71(1), 35–56. https://doi.org/10.1007/s11423-022-10123-8

Makarov, A., & Andreev, P. (2022). AI-powered learning systems: Current trends and future prospects in adaptive education. Journal of Educational Technology, 41(5), 745-763.

Panadero, E., Broadbent, J., Boud, D., & Lodge, J. M. (2023). Personalised learning: The role of self-regulated learning in artificial intelligence-driven systems. Educational Psychology Review, 35, 42–60.

Pape, L., Green, S., & Harvey, S. (2020). Personalized learning through adaptive systems: A guide to implementation and outcomes. Journal of Educational Psychology, 112(5), 939-951.

Purwanto, A., Asbari, M., Santoso, P. B., et al. (2021). The challenges of distance learning during the COVID-19 pandemic: A case study in Indonesia. Journal of Education and Learning, 15(2), 95–106.

Seng, C. L., Tan, Y. F., & Lim, S. T. (2024). Personalized learning in the digital era: The role of artificial intelligence in adaptive learning environments. International Journal of Educational Research, 96(3), 22-38.

Suwignyo, A., Rahmawati, N., & Lestari, A. (2023). Pemanfaatan AI dalam pendidikan tinggi: Antara harapan dan kenyataan. Jurnal Ilmu Pendidikan, 39(1), 15–28.

Tzima, S., Stylios, C. D., & Hadjileontiadis, L. J. (2020). An adaptive learning platform based on artificial intelligence for supporting personalized learning and learning analytics. International Journal of Artificial Intelligence in Education, 30, 223–248.

Utami, S. N., & Widodo, H. (2022). Tantangan integrasi kecerdasan buatan dalam pembelajaran digital di Indonesia. Jurnal Teknologi dan Pembelajaran, 14(2), 89–101. https://doi.org/10.21009/jtp.v14i2.1234

UNESCO. (2023). Technology in Education: A Tool on the Path to Inclusion. Paris: United Nations Educational, Scientific and Cultural Organization.

VanLeeuwen, A., & Rummel, N. (2020). Teacher support in the age of AI: How artificial intelligence can and should support teachers. British Journal of Educational Technology, 51(4), 987–1000.

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(1), 23–35.

Xie, H., Liu, X., & Xing, W. (2020). A systematic review of AI-driven educational analytics in higher education. Journal of Educational Computing Research, 58(6), 1066–1102.

Yunita, D. A., Sari, D. K., & Firmansyah, R. (2021). Eksplorasi kesiapan guru dalam penerapan pembelajaran berbasis teknologi AI di sekolah menengah. Jurnal Penelitian Pendidikan, 21(2), 134–145. https://doi.org/10.17509/jpp.v21i2.34567

Zhang, Q., Liu, Y., & Wang, J. (2023). Real-time adaptive feedback in AI-based learning systems: A comprehensive review. Journal of Learning Analytics, 12(1), 89-104.

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. https://doi.org/10.1186/s41239-019-0171-0

Downloads

Published

2025-05-14

How to Cite

Supriyatmoko, S., Anam, K., & Kurniawan, W. (2025). MODEL PEMBELAJARAN ADAPTIF BERBASIS KECERDASAN BUATAN: PELUANG DAN TANTANGAN DALAM MEWUJUDKAN PENDIDIKAN PERSONALISASI . STRATEGY : Jurnal Inovasi Strategi Dan Model Pembelajaran, 5(1), 36-45. https://doi.org/10.51878/strategi.v5i1.4944

Issue

Section

Articles

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.