Proposing A Critical AI Literacy Framework for Academic Librarians - Cecilia Zhang; Bing Wang; Shuqi Ye; Khamo (International Journal of Librarianship (IJoL))
This article investigates the application of Critical AI Literacy in research strategies in the field of humanities, exemplified by a case study of CNKI SMART, an AI-driven feature in one of the leading databases for Chinese Studies, the China National Knowledge Infrastructure (CNKI). As GenAI transforms information access, libraries and librarians bear increasing responsibility for fostering AI Literacy and Critical AI Literacy among patrons to support thoughtful engagement with GenAI-generated content. Addressing this critical gap, we propose a Critical AI Literacy evaluation practice—the RACBAC Standard—combining seven Critical AI Literacy Skills to assist researchers in humanities and potentially other disciplines. The RACBAC Standard evaluates GenAI outputs based on Relevance, Accuracy, Coverage, Bias, Authority, and Currency. Through a case study evaluating CNKI SMART’s responses to a given research question, we demonstrate the application of our proposed RACBAC Standards. Findings highlight the necessity of librarians’ role in assisting researchers to cross-reference and critically examine GenAI-assisted research using the proposed six standards. This article contributes to the emerging discourse on AI Literacy and Critical AI Literacy by advancing strategies that promote responsible use of GenAI tools across academic fields.