Modelling the impact of generative artificial intelligence on information behaviour research
Thomas D. Wilson · Journal of Documentation · 2026
Purpose Existing models of information behaviour assume interaction with passive retrieval systems such as search engines and bibliographic databases. Generative AI systems, which actively generate personalised responses rather than retrieving existing documents, represent a fundamental shift that these models do not address. This paper proposes a modification of Wilson's generic model of information behaviour to accommodate generative AI as an active intermediary. Design/methodology/approach Conceptual analysis grounded in a scoping review of 89 papers addressing AI-mediated information seeking, information ecosystem implications and theoretical gaps in existing frameworks. Cited reference searches on Wilson and Hirvonen et al. (2024) supplemented keyword searches in Google Scholar and Web of Science. Findings Six characteristics of generative AI require theoretical accommodation: opacity, epistemic authority, sycophancy risk, personalisation, first-person voice construction and commercial optimisation. A modified model is proposed introducing ambient activation as a new activating mechanism; AI literacy, trust calibration and vulnerability status as new intervening variables; the AI system as an active intermediary node; ethical evaluation as a new stage between information processing and use; and bidirectional feedback loops representing individual re-prompting and macro-level knowledge distribution effects. Originality/value The paper moves beyond applying existing information behaviour models to new contexts, proposing structural revision of Wilson's model to accommodate what generative AI systems are, rather than merely what users do with them.