Artificial intelligence (AI) is rapidly becoming integral to research and learning in higher education. Academic librarians, as information literacy experts, are uniquely positioned to help students develop **AI literacy** – the knowledge and skills to understand and use AI effectively and ethically. The Digital Education Council (DEC) @digitaleducationcouncilDigitalEducation AI Literacy Framework defines five key dimensions of AI literacy for all learners. This guide is organized around those five dimensions, illustrating the role librarians can play in each, along with practical teaching strategies, tools, and activities. It concludes with recommendations for a student-facing AI Literacy Workbook aligned to these dimensions. The focus is on actionable approaches in a higher education context, grounded in current best practices in academic libraries.

In an era of rapid digital transformation and expanding knowledge infrastructures, the pursuit of doctoral education by librarians is vital for fostering innovation, leadership, and equity in the library and information science (LIS) profession. However, the factors influencing librarians’ intent to undertake doctoral studies remain underexplored, particularly in developing contexts such as the Philippines. This study leverages the nationwide Philippine Librarians Census dataset and employs supervised machine learning techniques (logistic regression and random forest models) to predict which librarians are most likely to aspire to Ph.D. study within five years. Results reveal that ongoing academic engagement (current enrollment in further studies), professional role, years of service, and institutional type are among the strongest predictors of doctoral intent. Model performance was moderate (AUC equals 0.69), with both models highlighting similar key features. The findings underscore persistent disparities related to sector and region, suggesting that targeted support and policy interventions are needed to ensure equitable access to advanced LIS education. This study demonstrates the value of data-driven approaches for workforce planning and contributes new empirical evidence to guide educational and institutional strategies in digital library environments.

This paper investigates how structured training in information evaluation shapes the cognitive and attitudinal defenses that individuals deploy when encountering potential misinformation online. We examine two constructs—conflict detection, defined as the skill to recognize contradictions in information, and open-mindedness, the willingness to consider opposing viewpoints—across two student populations: those enrolled in Library and Information Science (LIS) programs and their non-LIS peers. By administering a cross-sectional survey that measures performance on the Cognitive Reflection Test, scores on the Actively Open-minded Thinking scale, and scenario-based sharing intentions, we test whether formal training amplifies the protective effects of these traits. Using hierarchical regression and interaction analyses, we evaluate differences in main effects and moderation patterns between the groups. Results will clarify how dual-process theories and meta-cognitive attitudes interact to curb misinformation sharing and will inform the design of targeted educational interventions that bolster digital literacy.