Enhancing Research on Engineering Education: Empowering Research Skills through Generative Artificial Intelligence for Systematic Literature Reviews

This work explores the integration of Artificial Intelligence (AI) to improve research skills in engineering education, specifically at the time of conducting a Systematic Literature Review (SLR). While AI has previously been employed for basic tasks related to SLRs, such as relevant paper screening and classification within scope, there is still limited research on information retrieval from the full texts of scientific papers. This study focuses on the next phase in the SLR, which involves scientific documentary analysis. The methodology presented in this work compares the AI tools in the market to facilitate scientific documentary analysis and enhance and refine research skills in engineering students. Also, this paper addresses the challenges to automatically run documentary analysis during a SLR. To do so, several techniques to extract information from AIs are presented, including Application Programming Interfaces (API) or Graphical User Interfaces (GUI). According to the results, most of the AI tools have a limitation in the operations students can perform per day. Only PDFgear offers a no-cost solution with unlimited usage, while some other AI tools allow limited usage for not so high prize. In conclusion, this paper presents a contribution during the documentary analysis of a SLR. It is important to keep in mind the constraints associated with the limited usage of AIs, the accuracy of AI tools, or the complexity of the developed scripts for automating the process. Due to the rapidly changing market for artificial intelligence, the validity of this study is limited to the current state of the tools. Similar studies will be necessary as AI tools continue to evolve.