‘GenAI’ Literature Search Tools and Scholarly Diversity: An Algorithmic Ethnographical Analysis

The rapid integration of Generative AI tools into academic research raises critical questions about their impact on epistemic justice in scholarly knowledge dissemination. This study examines whether ‘GenAI’ literature search tools amplify or lessen any biases compared to ‘traditional’ literature search databases. Adopting algorithmic ethnography, we analysed 800 search results consisting of the top 20 journal articles in four topics in higher education across 10 literature search platforms: five ‘GenAI’ tools and five ‘traditional’ databases. Metrics included first-author gender, geographic affiliation, numbers of citations and journal impact factors. We found that the search results from ‘GenAI’ literature search tools exhibited substantially greater geographic diversity but showed no consistent advantage in terms of gender balance. Both ‘GenAI’ literature search tools and ‘traditional’ literature search databases prioritised high-citation numbers and high-JIF publications. This paper contributes to the literature that looks beyond perceptions of ‘GenAI’ literature search tools and examines their outputs. The insights gained are useful for researchers and scholars making decisions regarding which platforms to use when conducting searches of academic literature and for those advising them.