An Exploratory Study of Helping Undergraduate Students Solve Literature Review Problems Using Litstudy and NLP

Abstract

(1) Many undergraduate students struggle to produce a good literature review in their dissertations, as they are not experienced, do not have sufficient time, and do not have the required skills to articulate information. (2) Subsequently, we deployed Litstudy and NLP tools and developed a recommendation system to analyze articles in an academic database to help the students produce literature reviews. (3) The recommendation system successfully performed three levels of analysis. The elementary-level analysis provided demographic statistical analysis to the students, helping them understand the background information of the selected articles they would review. The intermediate-level analysis provided visualization of citations in network graphs for the students to understand the relationships of the articles’ authors, regions, and institutes so that the flow of ideas, development, and similarity of the selected articles can be better analyzed. The advanced level of analysis provided topic modeling functions for the students to understand the high-level themes of the selected articles to improve productivity as they read through them and simultaneously boost their creativity. (4) The three levels of analysis successfully analyzed the selected articles to provide innovative results and triggered the students to handle literature reviews in a new way. Further enhancement opportunities were identified in integrating the NLP technologies with large language models to facilitate the generation of research ideas/insights. This would be an exciting opportunity to have AI/NLP integrated to help the students with their research.

Keywords

• literature review
• topic modeling
• NLP
• litstudy
• AI
• LLM