Fragmented AI Adoption in Indonesian Special Education: An Adaptive Multi-Layer Iterative Model for Differentiated Instruction in Intellectual Disability Contexts - Ajeng Suryani; Budi Susetyo; Iding Tarsidi; Hana Qurrotul Uyun; Mennatallah Elsabagh (LPPM Universitas Pendidikan Mandalika, 2026)
This study aims to investigate patterns of AI utilization for students with intellectual disabilities and develop the Adaptive Multi-Layer Iterative Model (AMLIM) as a conceptual framework for integrating AI into differentiated instruction practices for students with intellectual disabilities in Indonesia. Using a qualitative-dominant mixed-methods design, this study combines systematic literature synthesis and survey data from 80 special education teachers from public and private special schools across multiple provinces in Indonesia, predominantly West Java and South Sumatra. Data were analyzed using descriptive statistics and thematic synthesis. Results show that 81.5% of teachers perceived AI as beneficial for instructional material development, but only 30.9% had received formal AI-related training, while 85.2% relied primarily on social media for AI-related knowledge. Although 64.2% adapted AI-generated materials to students’ individual needs, implementation remains fragmented and concentrated in instructional preparation rather than systematic assessment and documentation. To address these limitations, this study proposes the Adaptive Multi-Layer Iterative Model (AMLIM), a feedback-based framework integrating pedagogical processes, adaptive personalization, assessment documentation, and governance structures. The model promotes a more systematic, documented, and accountable approach to AI-supported differentiated instruction in special education.