AI-Generated Video Production for Teaching Hijaiyah Letter Recognition: A Study of UIN Bandung's Digitalization Community Service Program
Keywords:
generative AI, hijaiyah, production model, community service, instructional videoAbstract
The increasing adoption of digital media in early childhood Islamic education has created urgent demand for animated instructional videos that accurately represent Qur’anic content, particularly hijaiyah letters. However, the capacity gap between Islamic educational institutions and professional video production remains a persistent structural challenge. Generative AI tools offer a promising democratization pathway, yet their systematic application for Arabic script-based educational content remains undocumented in the literature. This article reports an instrumental case study embedded within a community service program (Pengabdian kepada Masyarakat/PPM) on learning digitalization, conducted by an academic team from the Qur’anic Sciences and Tafsir study program at UIN Sunan Gunung Djati Bandung. The study documents the full production process of a 28-episode animated instructional video series introducing hijaiyah letters for PAUD/kindergarten-level learners, using Dreamina (character generation), Grok AI (video animation), and Corel VideoStudio (editing and post-production). Through systematic logging and analysis of eight iterative AI video generation attempts, five categories of consistent AI failure were identified: calligraphic distortion (100% occurrence rate), lip-sync error (63%), character proportion inconsistency (38%), non-contextual visual improvisation (63%), and dialogue assignment error (25%). Critically, calligraphic distortion—the most pedagogically consequential failure—manifested across all eight iterations without exception, confirming the structural “affordance disparity” between Latin and Arabic script rendering documented by Sobhan et al. (2025). From this failure taxonomy, the study constructs the Hybrid AI Production Workflow (HAPW) model, which strategically allocates tasks between a Generative Layer (AI) and a Precision Layer (human editor), with explicit quality gates at content-critical junctions. The HAPW model offers the first domain-specific framework for AI-assisted Islamic instructional media production, grounded in the Cognitive Theory of Multimedia Learning and validated against systematic field failure patterns. Its design as a non-technical, low-cost framework makes it directly transferable to other PTKI-based community service contexts across Indonesia.
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