Evaluating the Efficacy of AI Tools in Systematic Literature Reviews: A Comprehensive Analysis

Artificial Intelligence (AI) tools can revolutionize literature review practices by transforming the research landscape towards more efficient and reliable review processes. While conducting literature can be challenging and time-consuming, there is a plethora of AI powered tools which uncover potential solutions to the challenge. AI tools may reduce the time spent on repetitive tasks, allowing scholars to focus more on critical analysis and interpretation. Due to the rising abundance of AI tools, it is difficult to decide which AI tools are best for individual research problems or projects. While concerns exist around the ethical and quality consequences of using AI. The study aims to explore the usage of AI tools on the systematics literature review process, specifically focusing on their effectiveness in various stages and ethical concerns. IEEE and MDPI Journal papers from 2020 to 2024 were reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RobotReviewer, Covidence and EPPI-Reviewer are AI tools commonly used. These AI tools are designed to support different aspects of the systematic literature review process by offering capabilities such as problem formulation, literature search, inclusion screening and quality assessment. AI tools demonstrate improved effectiveness of literature searches, screening processes and data extraction. Language and content presentation, incorrect citation and plagiarism, grammar and spelling errors may be ren when utilizing AI. Concerns related to data quality, biases, and the need for human oversight were identified.

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Publication Details

Journal
Journal of Information Systems and Informatics
Published
2025-03-29
DOI
https://doi.org/10.51519/journalisi.v7i1.1035
Citations
4
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
8.43
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article

Evaluating the Efficacy of AI Tools in Systematic Literature Reviews: A Comprehensive Analysis

Mmatshuene Anna Segooa, Agnieta Pretorius, Phumzile Dorcus Mogoale, Refilwe Constance Mogase
4 citations
Journal of Information Systems and Informatics
Impact of AI and Big Data on Business and Society
8.43
article

Evaluating the Efficacy of AI Tools in Systematic Literature Reviews: A Comprehensive Analysis

Mmatshuene Anna Segooa, Agnieta Pretorius, Phumzile Dorcus Mogoale, Refilwe Constance Mogase
article en
4 citations

Abstract

Artificial Intelligence (AI) tools can revolutionize literature review practices by transforming the research landscape towards more efficient and reliable review processes. While conducting literature can be challenging and time-consuming, there is a plethora of AI powered tools which uncover potential solutions to the challenge. AI tools may reduce the time spent on repetitive tasks, allowing scholars to focus more on critical analysis and interpretation. Due to the rising abundance of AI tools, it is difficult to decide which AI tools are best for individual research problems or projects. While concerns exist around the ethical and quality consequences of using AI. The study aims to explore the usage of AI tools on the systematics literature review process, specifically focusing on their effectiveness in various stages and ethical concerns. IEEE and MDPI Journal papers from 2020 to 2024 were reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RobotReviewer, Covidence and EPPI-Reviewer are AI tools commonly used. These AI tools are designed to support different aspects of the systematic literature review process by offering capabilities such as problem formulation, literature search, inclusion screening and quality assessment. AI tools demonstrate improved effectiveness of literature searches, screening processes and data extraction. Language and content presentation, incorrect citation and plagiarism, grammar and spelling errors may be ren when utilizing AI. Concerns related to data quality, biases, and the need for human oversight were identified.

Journal of Information Systems and InformaticsVol. 7(1)
Tshwane University of Technology (ZA)
Openalex Percentile: Top 3%
Impact of AI and Big Data on Business and Society
8.43
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