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Generative artificial intelligence for literature reviews

Gerit Wagner et al.

Journal of Information Technology2026https://doi.org/10.1177/02683962261425675article
AJG 4ABDC A*
Weight
0.50

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.

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https://doi.org/https://doi.org/10.1177/02683962261425675

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@article{gerit2026,
  title        = {{Generative artificial intelligence for literature reviews}},
  author       = {Gerit Wagner et al.},
  journal      = {Journal of Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/02683962261425675},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.