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Challenges to reproducibility and replicability have gained widespreadattention over the past decade, driven by a number of large replicationprojects with lukewarm success rates. A nascent work has emerged developingalgorithms to estimate, or predict, the replicability of published findings.The current study explores ways in which AI-enabled signals of confidence inresearch might be integrated into literature search. We interview 17 PhDresearchers about their current processes for literature search and ask them toprovide feedback on a prototype replicability estimation tool. Our findingssuggest that information about replicability can support researchers throughoutliterature review and research design processes. However, explainability andinterpretability of system outputs is critical, and potential drawbacks ofAI-enabled confidence assessment need to be further studied before such toolscould be widely accepted and deployed. We discuss implications for the designof technological tools to support scholarly activities and advancereproducibility and replicability.

Integrating measures of replicability into literature search: Challenges and opportunities