AI-Driven Optimization of Research Proposal Systems in Higher Education

Abstract

The research management system becomes necessary for smoother submission of proposals, reviews and approvals. This paper elaborates on using Artificial Intelligence (AI) for improving the Research Proposal Systems (RPS)— with specific applicability to improve proposal management processes; making them more efficient and accurate. Methodology: The paper presents a framework that deploys AI algorithms in the RPS to automatically perform vital tasks, lessen administrative burden and provide a detailed view via data-driven insights.First the research identifies the age-old challenges that have continuously experienced by higher education institutions in managing research proposals such as long submission and review cycles, criterion of evaluation does not match up with others, administrative bottlenecks. The AI-fueled RPS that our proposal is proposing solves these problems by automating a number of steps in the proposal process. AI models help automate these boring paperwork tasks — which include checking submissions, matching them with reviewers, and keeping track of deadlines from the first submission to peer reviews all the way to final approvals. This saves faculty and staff time for faster turnaround times and limits the potential for human errors.