Streamlining the Selection Phase of Systematic Literature Reviews (SLRs) Using AI-Enabled GPT-4 Assistant API
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Abstract (arXiv)
The escalating volume of academic literature presents a formidable challenge in staying updated with the newest research developments. Addressing this, this study introduces a pioneering AI-based tool, configured specifically to streamline the efficiency of the article selection phase in Systematic Literature Reviews (SLRs). Utilizing the robust capabilities of OpenAI's GPT-4 Assistant API, the tool successfully homogenizes the article selection process across a broad array of academic disciplines. Implemented through a tripartite approach consisting of data preparation, AI-mediated article assessment, and structured result presentation, this tool significantly accelerates the time-consuming task of literature reviews. Importantly, this tool could be highly beneficial in fields such as management and economics, where the SLR process involves substantial human judgment. The adoption of a standard GPT model can substantially reduce potential biases and enhance the speed and precision of the SLR selection phase. This not only amplifies researcher productivity and accuracy but also denotes a considerable stride forward in the way academic research is conducted amidst the surging body of scholarly publications.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2024.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic reviewcited by 20
- 2024Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviewscited by 6
- 2025Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviewscited by 1
- 2025Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews13 Oct 2025
- 2024The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review6 Sep 2024
- 2024Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews15 Jul 2024