A Study on the Adoption and Impact of AI Tools in Academic Research Data Management with Special Reference to Plaghar District

Abstract

Research Data Management (RDM) is a critical pillar of scholarly integrity and impact, yet it remains a labor-intensive and often undervalued process, particularly in resource-constrained environments. This study investigates the potential of Artificial Intelligence (AI) tools to automate key RDM tasks within the specific context of Plaghar District, a region facing challenges like limited funding, infrastructure, and specialized staff. Using a mixed-methods approach-a survey of 85 researchers and librarians, followed by 15 in-depth interviews-this research maps the current RDM landscape and identifies barriers to AI adoption. Findings reveal a significant awareness gap regarding AI solutions, compounded by pervasive barriers including unreliable internet, high costs, a pronounced skills gap, and a lack of institutional RDM policies. However, a strong undercurrent of motivation exists among researchers to improve their research impact. The study concludes by proposing a strategic, multi-level framework for sustainable AI-RDM integration, emphasizing the role of open-source tools, targeted capacity building, and institutional policy development. This research offers a model for similar regions seeking to harness AI to alleviate administrative burdens and enhance research data quality.

Ähnliche Arbeiten