Building upon the foundations laid in Part One (Issue 43.5), which explored the initial promises and entry points of artificial intelligence (AI), this collection presents a more complex and mature phase of the discourse. The ten papers featured here move beyond asking if or what AI can do, to critically examine how it is being integrated, for whom and with what consequences. They collectively paint a picture of a field in transition, grappling with the practical, pedagogical, ethical and epistemological realities of weaving AI into the very fabric of library and information work.This special issue reflects a significant broadening of scope. The studies encompass global educational reforms, evaluations of historical knowledge, user behavior modeling, public library case studies and novel applications in classification and robotics. A unifying thread, however, is the shift from viewing AI as a set of external tools to understanding it as a transformative force that reconfigures relationships: between teachers and students, between historical and contemporary knowledge, between users and systems and between libraries and their communities.The transformation begins at the source of professional identity: education. The paper “Reconfiguring global LIS education: AI integration, pedagogical roles, and epistemic challenges” provides a sobering cross-continental analysis. It reveals that the integration of AI into library and information science (LIS) curricula is uneven, often creating a disconnect between technical skill acquisition and critical, ethical reflection. The authors warn of a worrying trend towards “role alienation,” where educators risk becoming mere “technical facilitators” and students “system operators.” This powerful analysis issues a clarion call: LIS education must evolve to cultivate not just tool operators, but “information governors” capable of auditing, critiquing and shaping the algorithmic systems that will define our future information landscape.Complementing this macro-view of knowledge dissemination is a micro-analysis of knowledge itself. “Automated learning support literature classification using large language models via different strategies” reimagines the ancient library science task of classification through a cognitive lens. By developing a learning support literature classification (LSLC) scheme and testing various large language models (LLMs) for automation, the study does more than demonstrate technical feasibility (which it does successfully). It proposes a fundamental re-orientation: classification systems should be designed not just for inventory, but to actively scaffold human learning during search processes. This bridges core information science theory with cutting-edge AI, pointing toward more cognitively resonant knowledge organization.A critical function of our field is assessment, and AI is enabling novel frameworks for assessment. “Contemporary technological value of ancient Chinese medical books: An empirical patent citation analysis using large language models” showcases this brilliantly. By using LLMs to analyze patent citations, the authors develop a method to quantify the latent technological value embedded in historical texts. This moves the evaluation of cultural heritage beyond qualitative rarity or age, offering a data-driven model to demonstrate its ongoing relevance to modern innovation. It provides librarians and archivists with a powerful new rationale for preservation and a methodology to uncover hidden connections between past knowledge and present invention.Similarly, “Do AI-related papers in top management science journals contribute greater academic influence and online attention?” uses AI-driven text analysis to examine the impact of AI research within a specific discipline. The study finds that the correlation between AI topics and scholarly influence varies across sub-disciplines and time, which highlights that the value of AI research is not monolithic. It encourages a more sophisticated, field-specific understanding of how AI integrates into and influences scholarly communication.At the heart of information services is the human user, and several papers in this issue probe the complex dynamics of human-AI interaction. “Interactive Perceptions and Persuasion: How AI-Generated Content Influences Users” uses grounded theory to unpack the cognitive processes at play. The identification of both heuristic (e.g. technical features) and systematic (e.g. content quality) cues that shape persuasion is crucial for designing trustworthy AI interfaces. It reminds us that user acceptance is a psychological process, not just a technical outcome.The question of trust is directly tackled in “AI or human? A study of university students’ awareness of library reference services agents.” It is a simplified Turing test within a library context that yields a striking finding: students struggle to distinguish AI from human agents and often attribute superior knowledge to AI for complex tasks. This challenges assumptions about the irreplaceability of human expertise in reference and underscores an urgent need for both advancing AI literacy among users and strategically defining the unique value of human librarians in an AI-augmented ecosystem.Further exploring human factors, “Enhancing Information Credibility Assessment through AI: A Personality-Aware Fake News Detection Model Using BERT” innovatively integrates psychological profiling into misinformation detection. By inferring Big Five personality traits from user comments, the model demonstrates improved accuracy. This pioneering work opens a new frontier at the intersection of AI, behavioral science and information integrity, while also raising important ethical questions about user profiling that the field must address.The translation of AI potential into on-the-ground reality is, perhaps, most vividly captured in studies of public libraries. “AI in public libraries: A systematic review of global literature and an analysis of local practices in China” juxtaposes global trends with local realities. The multi-case study reveals a gap between the promising applications found in literature (e.g. personalized recommendations, smart management) and the constraints of funding, infrastructure and staff capacity in practice, especially within China’s centralized branch system. This paper is a vital corrective to techno-optimism, emphasizing that successful integration is a sociotechnical challenge requiring context-specific strategies, staff development and equitable policies.Demonstrating a profoundly user-centered implementation, “From Automation to Cognition: A Contextual Design Approach for Enhancing Elderly Patron Services with Proactive and Memory-Aware Library Robots” presents the field study of “OREO.” This robot, designed with proactive engagement and memory-aware dialogue, shows how AI can be embodied to provide compassionate, consistent service for elderly patrons. The study moves beyond automation to cognition and social agency, suggesting a future where robots act as persistent, personalized companions in the library space, augmenting rather than replacing human staff.Finally, “From preference to purpose: A reconfigurable recommendation framework for user-centered information access” addresses a core service with a novel adaptive design. By creating a recommendation system that dynamically reconfigures its logic based on inferred user intent and behavior level, the framework shifts the paradigm from static, preference-based filtering to dynamic, purpose-driven guidance. This represents a significant step toward intelligent systems that adapt to the evolving nature of human information needs.Collectively, the contributions in this issue mark a maturation in the conversation about AI in LIS. We are moving from isolated experiments to systemic considerations; from technical fascination to critical integration; from a focus on the agent to a focus on the interaction and the context.As guest editors, we are inspired by the depth and breadth of inquiry represented in this collection. These papers do not simply report on AI applications; they engage in the essential work of steering the development and adoption of these powerful technologies toward ends that are effective, equitable and profoundly human-centered. We invite readers to explore these contributions as both a report on the state-of-the-art and a compass pointing toward the responsible and transformative future of our field.
Bu et al. (Tue,) studied this question.