Conceptualizing an AI Module for HISinOne RES: Pilot Planning at FernUniversität in Hagen
Author(s)
FernUniversität in Hagen
Kummert, Christina
FernUniversität in Hagen
Issue Date
May 22, 2026
Publisher
euroCRIS
Type
Conference Paper
Abstract
Modern research institutions face increasing challenges in managing and leveraging large volumes of heterogeneous research information. Current Research Information Systems (CRIS/FIS), such as HISinOne RES, provide structured environments for recording publications, projects, funding, collaborations, and patents, and thus support reporting and strategic decision-making. However, despite their strengths, traditional systems operate predominantly retrospectively, offering static insights rather than proactive guidance. This lack of forward-looking functionality limits the timely detection of emerging research trends, identification of optimal collaboration opportunities, and evidence-based evaluation of funding applications. Increasing complexity in research landscapes, disciplinary diversity, and administrative workloads intensify these challenges. Institutions therefore require solutions capable not only of handling large, heterogeneous datasets but also of transforming them into actionable insights for researchers, administrators, and institutional leadership. Addressing this gap forms the central motivation of the present project.
To tackle these challenges, we propose a modular Artificial Intelligence (AI) component designed to extend HISinOne RES with predictive analytics, semantic enrichment, automated classification, and collaboration recommendations. Unlike conventional add-on tools, the proposed AI module is conceived as an integrative layer that operates directly within the existing CRIS/FIS infrastructure, preserving established workflows and governance structures. Key design principles include data privacy compliance, explainability,
transparency, and adherence to ethical AI practices, ensuring that the generated insights remain interpretable and trustworthy for diverse user groups. The module will provide predictions on emerging research areas, highlight promising collaboration networks, and estimate the potential success of funding applications, thus supporting strategic decision-making at multiple institutional levels.
Description
14 slides.-- Presentation delivered within CRIS2026 session "AI-Enhanced CRIS for Decision Support, Prediction, and Policy".-- Includes extended abstract
URI
https://dspacecris.eurocris.org/handle/11366/9335
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CRIS2026_paper-3_Azeroual-Kummert_AI-Module-for-HISinOne-RES_extended-abstract.pdf
Description
Extended abstract
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199.2 KB
Format
Adobe PDF
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Name
CRIS2026_Azeroual-Kummert_slides_Conceptualizing-AI-Module-forHISinOneRES.pdf
Description
Presentation
Size
3.65 MB
Format
Adobe PDF
Checksum (MD5)
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Conference(s)