Development of AI-Based Automated Research Paper Reviewer Suggestion Systems in Federal Polytechnic, Bida, Niger State
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Background of the Study
Research paper reviewing is an essential process in academic publishing, which ensures the quality and credibility of scholarly works. Traditionally, this process involves human reviewers who critically evaluate papers for their originality, relevance, and academic rigor. However, as the volume of academic research continues to rise, this method has become increasingly inefficient, leading to delays and subjective biases in the review process.
AI-based systems offer an innovative solution to this problem by automating the process of identifying appropriate reviewers based on the content of research papers. By using natural language processing (NLP) algorithms, machine learning, and data mining techniques, AI systems can analyze the paper’s topic, methodology, and references to recommend the most suitable experts for the review. These AI-driven systems can reduce the time spent by editors in identifying qualified reviewers, improve the objectivity of the review process, and enhance the quality of academic publishing.
Federal Polytechnic, Bida, Niger State, which is home to a growing number of research scholars, faces the challenge of managing the increasing demands of research paper review processes. This study aims to design and implement an AI-based automated research paper reviewer suggestion system to streamline the review process, improve its efficiency, and ensure the selection of appropriate reviewers.
Statement of the Problem
The research paper review process at Federal Polytechnic, Bida, is often delayed due to the manual and subjective selection of reviewers. As the institution’s research output increases, the need for a more efficient, automated, and objective system to match papers with qualified reviewers becomes evident. While AI has the potential to streamline this process, there is limited research on its application within Nigerian polytechnics for academic publishing.
Objectives of the Study
- To design and implement an AI-based automated research paper reviewer suggestion system at Federal Polytechnic, Bida.
- To evaluate the effectiveness and accuracy of the AI-based system in selecting appropriate reviewers for research papers.
- To identify the challenges and benefits of integrating AI into the academic publishing process at Federal Polytechnic, Bida.
Research Questions
- How effective is the AI-based research paper reviewer suggestion system in selecting suitable reviewers for academic papers?
- What is the impact of AI-based systems on the efficiency and timeliness of the research paper review process?
- What challenges and opportunities arise from the implementation of AI-based reviewer suggestion systems in Nigerian polytechnics?
Research Hypotheses
- The AI-based system selects more accurate and relevant reviewers compared to traditional manual methods.
- AI-based reviewer suggestion systems improve the speed and efficiency of the academic paper review process.
- The integration of AI-based systems in academic publishing faces challenges such as technology adoption, staff training, and data privacy concerns.
Significance of the Study
This study will contribute to improving the efficiency and accuracy of the research paper review process at Federal Polytechnic, Bida. The findings will benefit other institutions by providing insights into the implementation of AI in academic publishing, streamlining the review process, and enhancing the quality of academic research.
Scope and Limitations of the Study
The study will focus on the design and implementation of an AI-based automated research paper reviewer suggestion system at Federal Polytechnic, Bida, Niger State. The scope will be limited to research papers submitted within the institution and will not extend to external academic journals.
Definitions of Terms
AI-Based Reviewer Suggestion System: An AI-driven system that automates the process of identifying suitable reviewers for academic research papers.
Natural Language Processing (NLP): A branch of AI that focuses on the interaction between computers and human language, enabling the analysis and understanding of textual data.
Machine Learning: A type of AI that allows systems to improve from experience and data, without being explicitly programmed.
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