[Paper Review] Research information in the light of artificial intelligence: quality and data ecologies

Otmane Azeroual, Tibor Koltay|arXiv (Cornell University)|May 6, 2024

Big Data and Business IntelligenceBusiness, Management and Accounting3 citations

TL;DR

This paper proposes a comprehensive, interdisciplinary framework for integrating artificial intelligence (AI) into research information management (RIM) systems to improve data quality and literacy. It presents a phased process model for implementing AI in RIM, emphasizing collaboration across university departments to address incomplete and inaccurate research data, with the key contribution being a structured approach to harmonizing AI with institutional data ecologies and research information practices.

ABSTRACT

This paper presents multi- and interdisciplinary approaches for finding the appropriate AI technologies for research information. Professional research information management (RIM) is becoming increasingly important as an expressly data-driven tool for researchers. It is not only the basis of scientific knowledge processes, but also related to other data. A concept and a process model of the elementary phases from the start of the project to the ongoing operation of the AI methods in the RIM is presented, portraying the implementation of an AI project, meant to enable universities and research institutions to support their researchers in dealing with incorrect and incomplete research information, while it is being stored in their RIMs. Our aim is to show how research information harmonizes with the challenges of data literacy and data quality issues, related to AI, also wanting to underline that any project can be successful if the research institutions and various departments of universities, involved work together and appropriate support is offered to improve research information and data management.

Motivation & Objective

• To address the growing challenge of poor data quality and incomplete research information in academic institutions.
• To develop a structured, multi-phase process model for integrating AI into research information management (RIM).
• To promote data literacy and data quality awareness among researchers and institutional stakeholders.
• To foster institutional collaboration across departments to support sustainable RIM practices.
• To ensure that AI applications in RIM are implemented effectively and ethically within existing data ecologies.

Proposed method

• The authors propose a conceptual process model outlining the lifecycle of AI integration in RIM, from project initiation to operational deployment.
• The framework integrates interdisciplinary approaches from digital libraries, AI, and research information management.
• It emphasizes the role of professional RIM as a data-driven foundation for scientific knowledge processes.
• The model includes phases for needs assessment, technology selection, data preparation, AI implementation, and ongoing monitoring.
• The approach is designed to be adaptable to various institutional contexts and data ecosystems.
• It advocates for institutional support structures and cross-departmental collaboration to ensure success.

Experimental results

Research questions

• RQ1 How can AI technologies be effectively selected and integrated into research information management systems?
• RQ2 What are the key phases and components of a successful AI implementation in institutional RIM?
• RQ3 How can data quality and data literacy be improved through AI-enhanced RIM practices?
• RQ4 What institutional and collaborative structures are necessary to support AI in RIM?
• RQ5 How do existing data ecologies influence the success of AI applications in research information systems?

Key findings

• The proposed process model provides a clear, actionable framework for universities to implement AI in RIM systematically.
• Successful AI integration in RIM depends on strong institutional collaboration and dedicated support structures.
• Data quality and data literacy are critical enablers for trustworthy AI applications in research information systems.
• The framework demonstrates that AI can be effectively embedded in RIM to correct and enhance incomplete or inaccurate research data.
• The model supports sustainable, scalable, and ethically sound AI deployment within academic data ecosystems.
• The study highlights that professional RIM is not only a technical tool but a strategic enabler for scientific knowledge processes.