The Quality of Multi Creative Works of Grade XI Students Oriented to Learning Style in Learning the C-R-E-A-T-E Model Assisted by Artificial Intelligence (AI) in the Making of Natural Material Buffer Solutions - Aliya Bidayatunnaja; Wawan Wahyu; Asep Suryatna (Edumaspul: Jurnal Pendidikan, 2026)
Penelitian ini bertujuan menganalisis kualitas multi karya kreatif murid kelas XI berdasarkan perbedaan gaya belajar melalui penerapan model pembelajaran C-R-E-A-T-E (Connecting, Restructuring, Elaborating, Applying, Tasking, Evaluating) berbantuan kecerdasan buatan (AI) pada topik pembuatan larutan penyangga berbahan alami. Metode yang digunakan adalah deskriptif kualitatif dengan subjek satu kelas XI IPA di SMAN 13 Bandung yang dipilih melalui convenience sampling. Instrumen utama adalah lembar penilaian multi karya kreatif berdasarkan lima indikator kreativitas Williams (fluency, flexibility, originality, elaboration, evaluation), yang dianalisis menggunakan model Miles, Huberman, dan Saldaña. Multi karya yang dinilai meliputi produk larutan penyangga alami, video kreatif, makalah ilmiah, dan presentasi PowerPoint. Hasil penelitian menunjukkan rata-rata kualitas multi karya kreatif sebesar 86,7%, dengan capaian tertinggi pada murid bergaya belajar visual (92,0%), diikuti auditori (87,8%), audio visual (85,7%), dan kinestetik (81,4%). Analisis per indikator mengungkap bahwa fluency dan flexibility dikuasai sangat baik (93,75–100%), sementara elaboration menjadi indikator terlemah khususnya pada murid visual (75,00%) dan originality terendah pada murid kinestetik (81,25%). Temuan ini mengonfirmasi bahwa model C-R-E-A-T-E berbantuan AI efektif memfasilitasi pencapaian multi karya kreatif secara inklusif, namun scaffolding yang lebih terdiferensiasi berdasarkan gaya belajar diperlukan terutama pada tahap Elaborating untuk murid visual dan kinestetik. Abstract This study aims to analyze the quality of multi-creative works of grade XI students based on differences in learning styles through the application of the C-R-E-A-T-E (Connecting, Restructuring, Elaborating, Applying, Tasking, Evaluating) learning model assisted by artificial intelligence (AI) on the topic of making natural material buffer solutions. The method used was qualitative descriptive with the subject of one class XI Science at SMAN 13 Bandung which was selected through convenience sampling. The main instrument was a multi-creative work scoresheet based on five Williams creativity indicators (fluency, flexibility, originality, elaboration, evaluation), which were analyzed using the Miles, Huberman, and Saldaña models. The multi-works assessed include natural buffer solution products, creative videos, scientific papers, and PowerPoint presentations. The results showed an average quality of multi-creative works of 86.7%, with the highest achievement in visual learning style students (92.0%), followed by auditory (87.8%), audio visual (85.7%), and kinesthetic (81.4%). Analysis per indicator revealed that fluency and flexibility were mastered very well (93.75–100%), while elaboration was the weakest indicator, especially in visual students (75.00%) and the lowest originality in kinesthetic students (81.25%). These findings confirm that the AI-assisted C-R-E-A-T-E model effectively facilitates the achievement of multi-creative work in an inclusive manner, but more differentiated scaffolding based on learning style is needed especially at the Elaborating stage for visual and kinesthetic students.