PENGGUNAAN TEKNOLOGI WEB UNTUK SISTEM E-LEARNING ADAPTIF DI PENDIDIKAN

Abstract

Perkembangan teknologi web telah mempercepat adopsi sistem e-learning adaptif, namun literatur menunjukkan masih adanya variasi pendekatan, keterbatasan integrasi kecerdasan buatan, serta belum adanya pemetaan komprehensif mengenai tren riset di bidang ini. Penelitian ini melakukan Systematic Literature Review terhadap 20 studi terbitan 2020–2025 untuk mengidentifikasi perkembangan, tantangan, dan peluang pengembangan e-learning adaptif berbasis web. Hasil kajian menunjukkan bahwa integrasi machine learning, learning analytics, dan teknik personalisasi konten semakin umum diterapkan dan terbukti meningkatkan keterlibatan serta hasil belajar. Meski demikian, sebagian penelitian masih berfokus pada pendidikan tinggi, belum memanfaatkan analitik real-time secara optimal, dan minim pembahasan mengenai kebutuhan regulasi etika AI. Secara khusus, studi ini menemukan tiga tren utama, yaitu: (1) integrasi machine learning dengan learning analytics real-time, (2) desain adaptif yang lebih inklusif untuk beragam kebutuhan dan gaya belajar peserta didik, dan (3) urgensi regulasi etika kecerdasan buatan di bidang pendidikan, serta mengidentifikasi gap riset pada pendidikan vokasi dan non-formal yang masih jarang dieksplorasi. Temuan ini memberikan arah penelitian lanjutan serta rekomendasi bagi pengembangan sistem e-learning adaptif berbasis web yang lebih efektif dan berkelanjutan.The development of web technology has accelerated the adoption of adaptive e-learning systems; however, existing literature indicates variations in approaches, limited integration of artificial intelligence, and a lack of comprehensive mapping of research trends in this field. This study conducts a Systematic Literature Review of 20 studies published between 2020 and 2025 to identify developments, challenges, and opportunities in web-based adaptive e-learning. The findings reveal that the integration of machine learning, learning analytics, and content personalization techniques is increasingly implemented and has been shown to improve learner engagement and learning outcomes. Nevertheless, many studies still focus primarily on higher education, have not fully optimized real-time analytics, and provide limited discussion on the need for ethical regulation of artificial intelligence. Specifically, this study identifies three major trends: (1) the integration of machine learning with real-time learning analytics, (2) more inclusive adaptive designs addressing diverse learner needs and learning styles, and (3) the growing urgency of ethical AI regulation in education. In addition, this review highlights research gaps in vocational and non-formal education, which remain underexplored. These findings provide directions for future research and recommendations for the development of more effective and sustainable web-based adaptive e-learning systems.