Gaps in artificial intelligence-related information behaviour research: a review of reviews

Abstract

Introduction. This study identifies gaps in AI-related research within information behaviour by analysing review articles that examine how AI systems reshape information seeking, evaluation, and use. Methods. The AI search system Perplexity was used alongside Web of Science and Google Scholar to identify relevant review articles. Publications were screened for relevance to information behaviour and AI. Analysis. Twelve English-language reviews published between 2018 and 2025 were analysed thematically using Perplexity for initial theme identification, followed by manual thematic analysis by both authors. Formal characteristics including author affiliations, publication venues, and citation metrics were also examined. Results. Three research strands emerged: conceptual frameworks (information ecology), domain-specific studies (health, consumer behaviour, academic practice), and methodological contributions. Reviews documented AI benefits (efficiency, accessibility) alongside persistent concerns about credibility, bias, and deskilling. Significant theoretical gaps remain, particularly regarding AI literacy and trust dynamics. Conclusions. AI fundamentally alters information behaviour rather than merely enhancing it. Research reveals profound ambivalence: users appreciate AI's convenience whilst harbouring concerns about accuracy and control. Longitudinal studies, cross-cultural research, and theoretical development are urgently needed.

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