AI-generated predictions for library futures: a comparative large language model analysis
DOI:
https://doi.org/10.47989/ir31263015Keywords:
Artificial intelligence, Future of libraries, Large language models, Data-driven insights, Library servicesAbstract
Introduction. This research examines Artificial Intelligence (AI) tools' predictions regarding the future of libraries within the framework of five fundamental elements: Building, collection, personnel, budget, and users. While existing literature examines AI’s impact on library services from human perspectives, this study explores how AI tools themselves envision the future of libraries by analysing their outputs across different models.
Method. A qualitative approach was employed, consisting of structured question-answer sessions with five large language models (LLMs): ChatGPT, Claude, Grok, DeepSeek, and Gemini.
Analysis. Responses were analysed using inductive content analysis to identify common themes and divergent perspectives.
Results. Findings indicate strong paradigmatic convergence across all models. Despite differences in infrastructure, institutional origin, and training data, all tools anticipate a shift from libraries as static repositories to dynamic learning centres. Shared themes include hybrid physical–digital structures, personalisation, human–AI collaboration, and ethical governance. Collections are expected to become format-agnostic and open access–oriented; buildings to evolve into modular, multidisciplinary spaces; users to demand instant, personalised services; personnel to assume strategic, AI-literate advisory roles; and budgets to prioritise digital infrastructure and services.
Conclusion. By analysing AI tools’ own discourse, this study contributes novel, data-driven insights for library managers, educators, and policymakers, while also demonstrating both the potential and limitations of LLMs as instruments for consistent analytical prediction.
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