SYSTEMATIC LITERATURE REVIEW: INTEGRASI ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN BIOLOGI ERA DIGITAL

Raniakbar, Ari Syahara (2025) SYSTEMATIC LITERATURE REVIEW: INTEGRASI ARTIFICIAL INTELLIGENCE DALAM PEMBELAJARAN BIOLOGI ERA DIGITAL. Diploma thesis, UIN RADEN INTAN LAMPUNG.

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Abstract

Abstrak: Penelitian ini bertujuan untuk mengkaji tren, manfaat, dan tantangan integrasi Artificial Intelligence (AI) dalam pembelajaran biologi melalui pendekatan Systematic Literature Review (SLR). Masalah yang diangkat adalah minimnya kajian sistematis terkait pemanfaatan AI di pendidikan biologi era digital. Metode SLR digunakan dengan mengikuti prosedur PRISMA, mencakup identifikasi, seleksi, analisis, dan sintesis literatur relevan lima tahun terakhir. Hasil kajian menunjukkan bahwa AI mampu meningkatkan efektivitas pembelajaran biologi melalui personalisasi, visualisasi konsep abstrak, serta peningkatan motivasi dan prestasi siswa. AI juga mendukung guru dalam evaluasi pembelajaran dan perancangan strategi pengajaran yang inovatif. Namun, tantangan utama mencakup keterbatasan infrastruktur digital, rendahnya literasi teknologi, dan resistensi pendagogik. Penelitian ini 2 menyimpulkan bahwa integrasi AI memiliki potensi besar dalam mentransformasi pembelajaran biologi, namun perlu didukung kebijakan, pelatihan guru, dan kolaborasi lintas sektor. Implikasi teoretis dari penelitian ini memperkaya literatur pendidikan biologi berbasis teknologi, sementara secara praktis menjadi rujukan bagi pengambilan kebijakan dan pengembangan kurikulum di Indonesia. Kata kunci: Kecerdasan Buatan, Pembelajaran Biologi, Tinjauan Literatur Sistematis. Abstract: This study aims to examine the trends, benefits, and challenges of integrating Artificial Intelligence (AI) into biology education through a Systematic Literature Review (SLR) approach. The issue addressed is the lack of systematic research regarding the use of AI in biology education, particularly in the digital era. The SLR method follows PRISMA procedures, including identification, selection, analysis, and synthesis of relevant literature from the past five years. Findings indicate that AI can enhance the effectiveness of biology learning through personalization, visualization of abstract concepts, and improved student motivation and performance. AI also assists teachers in assessment and the design of innovative teaching strategies. However, key challenges include limited digital 3 infrastructure, low technological literacy, and pedagogical resistance. This study concludes that AI integration has strong potential to transform biology education but requires support through policy, teacher training, and cross-sector collaboration. Theoretically, it contributes to the body of technology-based biology education literature, while practically serving as a reference for policy-making and curriculum development in Indonesia. Keywords: Artificial Intelligence, Biologi Education, Systematic Literature Review.

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