Generative Artificial Intelligence for Literature Reviews
Journal of Information Technology · 2026
Wagner, Gerit 
Prester, Julian
Mousavi, Reza
Lukyanenko, Roman
Paré, Guy
literature-reviews
Abstract
Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this work outlines approaches for conducting literature reviews using both general-purpose (e.g., ChatGPT, Gemini, Claude) and specialized GenAI tools (e.g., Consensus, Elicit). We provide illustrative examples of prompts and suggest methodologically-sound literature review strategies. Throughout this perspective paper, we adopt a balanced approach considering both the opportunities and the risks of relying on GenAI in the conduct of literature reviews. We conclude by discussing philosophical questions related to the effects of GenAI on long-term scientific progress, and also present fruitful opportunities for research on improving the core of GenAI’s technology—its architecture and training data—and suggest open issues in GenAI-based literature reviews methodology.
Open access PDF
Citation (APA)
Wagner, G., Prester, J., Mousavi, R., Lukyanenko, R., & Paré, G. (2026). Generative Artificial Intelligence for Literature Reviews. Journal of Information Technology. https://doi.org/10.1177/02683962261425675
Citation: BibTeX
@article{WagnerPresterMousaviEtAl2026,
doi = {10.1177/02683962261425675},
author = {Wagner, Gerit and Prester, Julian and Mousavi, Reza and Lukyanenko, Roman and Paré, Guy},
journal = {Journal of Information Technology},
title = {Generative Artificial Intelligence for Literature Reviews},
year = {2026},
url = {https://journals.sagepub.com/doi/10.1177/02683962261425675},
abstract = {Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this work outlines approaches for conducting literature reviews using both general-purpose (e.g., ChatGPT, Gemini, Claude) and specialized GenAI tools (e.g., Consensus, Elicit). We provide illustrative examples of prompts and suggest methodologically-sound literature review strategies. Throughout this perspective paper, we adopt a balanced approach considering both the opportunities and the risks of relying on GenAI in the conduct of literature reviews. We conclude by discussing philosophical questions related to the effects of GenAI on long-term scientific progress, and also present fruitful opportunities for research on improving the core of GenAI’s technology—its architecture and training data—and suggest open issues in GenAI-based literature reviews methodology.}
}Citation: RIS
TY - JOUR
AU - Wagner, Gerit
AU - Prester, Julian
AU - Mousavi, Reza
AU - Lukyanenko, Roman
AU - Paré, Guy
TI - Generative Artificial Intelligence for Literature Reviews
T2 - Journal of Information Technology
PY - 2026
DO - 10.1177/02683962261425675
UR - https://journals.sagepub.com/doi/10.1177/02683962261425675
ER -