Building an AI-CRIS Platform: Multilingual, Contextual, and Predictive Research Data Access
Author(s)
Research Ecosystems
FernUniversität in Hagen
Issue Date
May 22, 2026
Publisher
euroCRIS
Type
Conference Paper
Abstract
Current Research Information Systems (CRIS) have become essential tools for universities and research institutions to manage, analyze, and disseminate information about research activities, outputs, and collaborations. Traditional CRIS platforms provide structured repositories for publications, projects, grants, and researcher profiles, supporting reporting obligations and strategic decision-making. However, these systems often operate retrospectively and rely on manual data retrieval, limiting their ability to provide dynamic, context-aware, and predictive insights. The increasing volume, heterogeneity, and complexity of research data require intelligent solutions that not only organize information but also enable proactive knowledge discovery and decision support. Addressing these challenges, this study presents the development of an AI-enhanced Current Research Information System (AI-CRIS), named GCRIS AI, implemented for the İzmir Institute of Technology.
The GCRIS AI module introduces a novel approach to research information management by integrating artificial intelligence capabilities directly into the CRIS environment. Its primary
component is a multilingual chatbot, which serves as an intelligent assistant for researchers, administrators, and institutional leadership. The chatbot leverages natural language processing (NLP) and semantic understanding to interpret user queries, retrieve relevant information from the institutional GCRIS database, and provide precise, contextual, and actionable responses.
Description
16 slides.-- Presentation delivered within CRIS2026 session "AI-Enhanced CRIS for Decision Support, Prediction, and Policy".-- Includes extended abstract
URI
https://dspacecris.eurocris.org/handle/11366/9302
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Name
CRIS2026_paper-2_Usta-et-al_GCRIS-AI_extended-abstract.pdf
Description
Extended abstract
Size
215.19 KB
Format
Adobe PDF
Checksum (MD5)
68953779d6901a1cf1de3803728f2885
Name
CRIS2026_Usta-et-al_slides_Building-an-AI-CRIS-Platform.pdf
Description
Presentation
Size
1.89 MB
Format
Adobe PDF
Checksum (MD5)
d6b0a51a9e960b3043ba7a0a1776ec57
Conference(s)