From card catalogs to chatbots: envisioning academic library services in the era of AI and LLMs
Chao CaiPurpose
This article introduces the CARDLG framework: Collections, Access, Retrieval, Description, Literacy and Governance. The framework analyzes how generative artificial intelligence (AI) and large language models reconfigure recurring library infrastructural dimensions rather than representing wholly unprecedented disruption.
Design/methodology/approach
The analysis draws on library history from manuscript repositories through digital discovery systems, tracing how each CARDLG dimension evolved across technological eras. It identifies continuities, accelerations and ruptures in AI/large language model (LLM) implementations compared to prior infrastructural shifts such as card catalogs, online public access catalogs and web-based discovery layers.
Findings
AI/LLMs extend familiar challenges including vendor dependence and opaque retrieval algorithms while introducing distinct risks tied to synthetic answer generation, training data governance and output accountability. The framework reveals how AI integration cuts across all six dimensions simultaneously, requiring coordinated decision-making across collections, systems, instruction and governance units.
Originality/value
This article provides a historically informed analytical framework for library practitioners navigating AI adoption. It offers CARDLG-based design principles, implementation scenarios across discovery, metadata, literacy instruction and vendor negotiation contexts, and stakeholder implications across library roles. The framework enables deliberate, values-aligned decisions rather than reactive adoption driven by vendor timelines.