Pengembangan Modul AI-Driven Micro-Tutoring Dengan Model Pembelajaran Case-Based Learning Untuk Meningkatkan Keterampilan Metakognitif dan Memecahkan Masalah Siswa SMP dalam Materi Cyberbullying

Development of an AI-Driven Micro-Tutoring Module Using a Case-Based Learning Model to Enhance Junior High School Students’ Metacognitive and Problem-Solving Skills in Cyberbullying Content

Kompleksitas dampak cyberbullying memerlukan intervensi pembelajaran yang adaptif dan reflektif. Penelitian ini bertujuan untuk mengembangkan modul AI-Driven Micro-Tutoring dengan model Case-Based Learning serta menguji kelayakan dan efektivitasnya terhadap peningkatan keterampilan metakognitif dan memecahkan masalah siswa pada materi cyberbullying. Penelitian ini menerapkan model pengembangan Holistic 4D (Define, Design, Develop, Deploy). Uji efektivitas dilakukan dengan desain Quasi-Experimental Nonequivalent Control Group pada siswa kelas VIII SMPN 13 Gresik. Data dianalisis menggunakan independent sample t-test. Hasil validasi ahli materi, media, desain, evaluasi pembelajaran, dan alpha test menunjukkan modul sangat layak dengan persentase 96,8%-100%. Uji penggunaan media (perorangan, kelompok kecil, beta test, hingga kelompok besar) juga mencapai kriteria sangat layak (90,4%-96,2%). Hasil uji efektivitas menunjukkan keterampilan memecahkan masalah meningkat signifikan (Sig. 0,045) dengan N-Gain 58,15 (sedang), mengonfirmasi efektivitas AI-scaffolding dalam memandu analisis kasus kompleks. Sebaliknya, keterampilan metakognitif memperoleh N-Gain 29,09 (rendah) dan tidak signifikan (Sig. 0,250). Temuan ini mengindikasikan adanya monitoring-control gap, di mana internalisasi strategi metakognitif merupakan proses perkembangan jangka panjang yang membutuhkan durasi intervensi lebih lama. Disimpulkan bahwa modul ini layak dan efektif sebagai bahan ajar inovatif, disertai rekomendasi integrasi berkelanjutan lintas materi guna memfasilitasi kematangan metakognitif siswa secara bertahap.

Kata Kunci: AI-Driven Micro-Tutoring, Case-Based Learning, Cyberbullying, Keterampilan Metakognitif, Keterampilan Memecahkan Masalah.

The complexity of cyberbullying impacts necessitates adaptive and reflective instructional interventions. This study aims to develop an AI-Driven Micro-Tutoring module utilizing Case-Based Learning and to examine its feasibility and effectiveness in enhancing students' metacognitive and problem-solving skills on cyberbullying content. This research employs the Holistic 4D development model (Define, Design, Develop, Deploy). The effectiveness test was conducted using a Quasi-Experimental Nonequivalent Control Group design involving eighth-grade students at SMPN 13 Gresik. Data were analysed using the independent sample t-test. Validation results from subject matter, media, instructional design, learning evaluation experts, and alpha test demonstrated that the module is highly feasible (96.8%–100%). Media usability tests across various stages also achieved highly feasible criteria (90.4%–96.2%). The effectiveness test revealed that problem-solving skills improved significantly (Sig. 0.045) with an N-Gain of 58.15 (medium category), confirming the effectiveness of AI-scaffolding in guiding complex case analysis. Conversely, metacognitive skills obtained an N-Gain of 29.09 (low category) and were not significant (Sig. 0.250). This finding indicates a monitoring-control gap, suggesting that the internalisation of metacognitive strategies is a long-term developmental process requiring an intervention duration exceeding short micro-tutoring. It is concluded that this module is feasible and effective as innovative instructional material, with a recommendation for sustainable cross-topic integration to facilitate gradual metacognitive maturation.

Keywords: AI-Driven Micro-Tutoring, Case-Based Learning, Cyberbullying, Metacognitive Skills, Problem-Solving Skills.