Search as learning: a bibliometric review

Abstract This review maps the intellectual base and evolution of Search as Learning using a reproducible workflow that combines a Web of Science corpus, Python screening, and VOSviewer, yielding 95 studies. Keyword co-occurrence, temporal overlays, co-citation, and country and journal mappings reveal eight thematic clusters and a shift from cognitive and exploratory themes to measurable learning outcomes and emerging AI mediated support. The co-citation results indicate a three part foundation: conceptual framing, operational measurement and prediction of knowledge gain, and log based behavioral methods. Collaboration concentrates in the United States and Northwestern Europe, with outlets centered on Conference on Human Information Interaction and Retrieval (CHIIR) and strong citation impact for the Journal of Information Science. Building on these findings, the review articulates an extended definition that unites user centered, interaction centered, and system centered perspectives, and proposes a simple prompt framework that operationalizes the system centered view in generative AI based SAL support.