AI-POWERED HADITH VERIFICATION: TOWARD A NEW MODEL OF AUTHENTICITY IN ISLAMIC KNOWLEDGE TRANSMISSION
Abstrak
The rapid expansion of digital Islamic knowledge has intensified concerns regarding the circulation of unauthenticated hadith, highlighting the need for scalable and methodologically grounded verification mechanisms in the digital age. Classical hadith scholarship offers a rigorous epistemological framework, yet the volume and complexity of textual data now exceed the capacity of manual verification. This study aims to conceptualize an AI-powered model for hadith authentication that aligns machine learning capabilities with the methodological principles of traditional hadith sciences. A qualitative exploratory design was employed, integrating document analysis of classical verification texts, contemporary academic studies, and computational research in natural language processing and network analysis. The findings show substantial structural parallels between classical authentication criteria—such as narrator reliability, chain continuity, and matn coherence—and computational techniques including graph modeling, semantic similarity analysis, and anomaly detection. The study concludes that artificial intelligence can function as a complementary analytical tool that enhances accuracy, efficiency, and scalability in hadith verification while preserving scholarly authority and epistemic integrity. The proposed conceptual model supports the development of hybrid verification systems capable of strengthening authenticity in digital Islamic knowledge transmission.
Identifier
- DOI: https://doi.org/10.70177/ijonis.v2i5.2836
- OpenAlex: https://openalex.org/W7169806167