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All Journal Edu Riligia: Jurnal Ilmu Pendidikan Islam dan Keagamaan EDU RILIGIA: Journal of Islamic and Religious Education
Karima, Via Nur
Universitas Islam Negeri Sunan Kalijaga
Religion Humanities Education Social Sciences Other

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Distorsi Kajian Hadis: Analisis Fenomena Halusinasi Artificial Intelligence (AI) Dalam Studi Hadis Nur Waarits, Muhammad Taufiqqulhidayah; Karima, Via Nur
EDU-RILIGIA: Jurnal Ilmu Pendidikan Islam dan Keagamaan Vol 10, No 1 (2026): Januari-Maret
Publisher : Pascasarjana Universitas Islam Negeri Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47006/er.v10i1.28100

Abstract

Hadith studies, as the second pillar of Islamic jurisprudence, are fundamentally anchored in the strict principles of authenticity and precision regarding sanad (chain of narration) and matan (text) data. While generative Artificial Intelligence (AI) offers efficiency, it introduces a critical methodological vulnerability known as "hallucination"-the tendency to generate fictitious yet convincing data. This research aims to analyze how AI hallucination specifically manifests as a methodological threat and data distortion in Hadith studies. Employing a descriptive analysis method with a comparative case study using a Large Language Model (LLM), the study compared AI responses concerning Hadith data (Sahih al-Bukhari from the Ibn Kathir print) and its commentary (syarah) (Ibn Daqiq Al-'Ied) against their primary sources. The findings identified three categories of distortion: Contextual Hallucination (misinterpreting "Dar Ibn Kathir Publisher" as "Imam Ibn Kathir"); Factual Hallucination (providing incorrect matan text for a specific Hadith number, No. 6309); and Linguistic Hallucination (fabricating academic-sounding but fictitious syarah analysis, such as the false claim of using the word (دعائم) and inventing a descriptive analysis by Ibn Daqiq). This research concludes that AI hallucination poses a real and verifiable threat to Hadith data integrity, urging urgent mitigation strategies, including improving training data quality and involving humans such as hadith experts to validate the data provided by AI.
Distorsi Kajian Hadis: Analisis Fenomena Halusinasi Artificial Intelligence (AI) Dalam Studi Hadis Nur Waarits, Muhammad Taufiqqulhidayah; Karima, Via Nur
EDU-RILIGIA: Jurnal Ilmu Pendidikan Islam dan Keagamaan Vol 10, No 1 (2026): Januari-Maret
Publisher : Pascasarjana Universitas Islam Negeri Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47006/er.v10i1.28100

Abstract

Hadith studies, as the second pillar of Islamic jurisprudence, are fundamentally anchored in the strict principles of authenticity and precision regarding sanad (chain of narration) and matan (text) data. While generative Artificial Intelligence (AI) offers efficiency, it introduces a critical methodological vulnerability known as "hallucination"-the tendency to generate fictitious yet convincing data. This research aims to analyze how AI hallucination specifically manifests as a methodological threat and data distortion in Hadith studies. Employing a descriptive analysis method with a comparative case study using a Large Language Model (LLM), the study compared AI responses concerning Hadith data (Sahih al-Bukhari from the Ibn Kathir print) and its commentary (syarah) (Ibn Daqiq Al-'Ied) against their primary sources. The findings identified three categories of distortion: Contextual Hallucination (misinterpreting "Dar Ibn Kathir Publisher" as "Imam Ibn Kathir"); Factual Hallucination (providing incorrect matan text for a specific Hadith number, No. 6309); and Linguistic Hallucination (fabricating academic-sounding but fictitious syarah analysis, such as the false claim of using the word (دعائم) and inventing a descriptive analysis by Ibn Daqiq). This research concludes that AI hallucination poses a real and verifiable threat to Hadith data integrity, urging urgent mitigation strategies, including improving training data quality and involving humans such as hadith experts to validate the data provided by AI.
Co-Authors Nur Waarits, Muhammad Taufiqqulhidayah