Generative AI has rapidly expanded the production of narrated educational videos, yet their linguistic accuracy, cultural fidelity, and pedagogical risks remain underexamined—particularly in Arabic-learning contexts. This paper proposes a conceptual framework for evaluating these risks by integrating findings on multimodal error pathways, Arabic-specific linguistic challenges, and automated detection–mitigation strategies. Using a pilot pipeline (GPT-4 → diffusion video synthesis → neural TTS → ASR → automated detectors → human validation), we model how linguistic, semantic, and visual errors propagate across modules and influence learner comprehension. The framework defines an Arabic-grounded error taxonomy, assesses detector reliability, and applies severity-weighted scoring to estimate pedagogical impact. It also introduces a multi-stage triage mechanism using automated checkpoints calibrated to institutional safety thresholds to support responsible deployment. Although the empirical sample is limited, the analysis highlights recurring error modes, especially dialect/register mismatches and script-level semantic drift, and identifies bottlenecks where human-in-the-loop review remains essential. The paper concludes with recommendations for classroom testing, dialect-aware evaluation, and scalable validation workflows. Overall, this work offers a structured approach for safely integrating generative AI into Arabic education by addressing error propagation, cultural alignment, and instructional risk in a systematic and context-appropriate manner.
Abdelrehim et al. (2026) studied this question.