Skip to content
#data science Open access

Reimagining Hadith Studies: Embodied Islamic Knowledge and the Integration of Religion and Science in Southeast Asia Islamic University

Oct 2026 · Dirasah International Journal of Islamic Studies

Abstract

The transformation of Islamic higher education institutions into universities has intensified demands to integrate religious sciences and modern disciplines, while also raising concerns about marginalizing hadith studies as a core normative field. This article reimagines hadith studies in the university era by examining how an embodied Islamic knowledge approach sustains their relevance within religion–science integration frameworks. Employing a qualitative comparative case study design, this research draws on fieldwork conducted at State Islamic University Syekh Wasil Kediri, State Islamic Institute Madura, State Islamic Institute Ponorogo in Indonesia, and Fatoni University in Southern Thailand. Data were collected through in-depth interviews, participant observation, and document analysis, and analyzed thematically. The findings show that although institutional transformation creates structural opportunities for interdisciplinary integration, formal policies and curricular reforms alone are insufficient to sustain hadith studies. Instead, hadith demonstrates resilience through embodied practices, including pesantren-based academic traditions, ethical pedagogical exemplification, community engagement, and applied ethical initiatives. Through these practices, hadith functions not merely as a textual reference but as a lived moral and epistemological foundation that mediates relations between normative Islamic knowledge and empirical sciences. This study argues that the sustainability of hadith studies depends on praxis-oriented and experience-based integration rather than purely cognitive or administrative models. Theoretically, it contributes to debates on religion–science integration and Islamic anthropology by conceptualizing hadith as lived and embodied knowledge within academic life.

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.

P. Abrahamsson, O. Salo, Jussi Ronkainen et al. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.

M. Pikkarainen, Jukka Haikara, O. Salo et al. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54

Trajectory Balance: Improved Credit Assignment in GFlowNets

It is proved that any global minimizer of the trajectory balance objective can define a policy that samples exactly from the target distribution, and empirically demonstrate the benefits of the trajectories balance objective for GFlowNet convergence, diversity of generated samples, and robustness to long action sequenc...

Esmeralda S. Whitammer, Moksh Jain, Emmanuel Bengio et al. · 302 citations · ⚡60

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.