THE COMPUTATIONAL CONVERGENCE OF AI AND ISLAMIC DIGITAL HUMANITIES

A CRITICAL ANALYSIS OF NLP APPLIED TO THE ARABIC LANGUAGE, QURAN, HADITH, AND ISLAMIC LITERATURE

Abstract

This study investigates the accelerating paradigm shift driven by Artificial Intelligence (AI) and Deep Learning architectures within Arabic computational linguistics and Islamic scriptural hermeneutics. Contemporary literature indicates that Natural Language Processing (NLP) provides robust frameworks for parsing complex Arabic morpho-syntactic structures and semantic layers, alongside automating translation and manuscript digitization. Specialized deep learning architectures—specifically domain-adapted Arabic BERT variants—have significantly mitigated systemic grammatical and semantic ambiguities. Conversely, deploying these systems introduces profound ethical, ontological, and theological vulnerabilities, including data-driven algorithmic biases and epistemic hallucinations within sacred text interpretations. To secure a resilient and doctrinally sound implementation, this paper proposes a governance matrix derived from the teleological objectives of Islamic jurisprudence (Maqasid al-Shariah). Through comprehensive case studies, empirical benchmarks, and comparative cross-lingual analyses, this research demonstrates that maximizing AI's utility for Islamic cultural heritage requires a strictly regulated equilibrium balancing technical automation with expert human oversight and foundational Islamic principles.