'AI' Literature Search Tools and Scholarly Diversity Project

Two datasets used for the 'AI' Literature Search Tools and Scholarly Diversity Project. This study examines whether 'AI' literature search tools amplify or lessen any biases compared to conventional literature search platforms. 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 AI-driven tools and five ‘non-AI’ traditional databases. Metrics included first-author gender, geographic affiliation, numbers of citations and journal impact factors. We found that the search results from 'AI' platforms exhibited significantly greater geographic diversity but showed no consistent advantage in terms of gender balance. Both 'AI' and ‘non-AI’ tools prioritised high-citation numbers and high-JIF publications. This work contributes to the literature that looks beyond perceptions of 'AI' literature search tools and examines their outputs. The insights gained will be useful for everyone making decisions regarding which platforms to use when conducting searches of academic literature and for those advising them. Description