AI‐Cinema is a hybrid neural‐symbolic framework addressing the critical challenge of preserving cultural authenticity in Arabic movie scenario generation. The framework integrates transformer‐based neural language models (AraT5‐base and AraGPT2‐medium) with symbolic reasoning encoded in OWL‐DL ontologies and SWRL rules to ensure linguistic fluency, narrative coherence, and cultural preservation. AI‐Cinema introduces a three‐tier architecture comprising a data layer, cultural embedding layer, and scenario generation layer. Central to its design is an attention‐based cultural embedding mechanism leveraging ArabicVerbNet (12,500 culturally annotated verbs) and ArabicNameNet (3653 names with regional annotations), complemented by a mathematically grounded harmony function that dynamically balances neural generation with symbolic constraints. In experiments on 2740 Arabic narratives, AI‐Cinema achieves a BLEU‐4 score of 32.76 (± 0.6), representing a 5.0% relative improvement over AraBERT‐Gen ( p < 0.01, paired bootstrap test) and a 27.5 percentage point gain in cultural preservation metric (CPM) compared with MARBERT‐Gen (82.3% vs. 54.8%). The framework maintains 92.3% dialectal accuracy across Modern Standard Arabic and six regional dialects, with explicit evaluation of code‐switching scenarios. Expert evaluations by 30 Arabic literature scholars demonstrate strong interannotator agreement (Fleiss’ κ = 0.78, p < 0.001), with 87% of evaluators rating generated narratives as culturally authentic (score ≥ 4 on a 5‐point scale). Current limitations include reduced performance on underrepresented dialects (Yemeni: 85.6% and Sudanese: 86.1%) and complex code‐switching scenarios (76.3% for 3+ dialects). The framework incorporates transparent labeling mechanisms for AI‐generated cultural content to address authenticity concerns. All resources are publicly available at https://github.com/Mossab82/AI-Cinema .
Ibrahim et al. (Thu,) studied this question.