For decades, the “search box” at the heart of the university library has been a gatekeeper. To unlock the vast treasures of academic databases, users had to speak a specific, rigid language, Boolean. For expert researchers, terms like AND, OR, and NOT are second nature. But for many students without appropriate information searching skills and training, the traditional search interface has often acted more as a barrier than a bridge.

In the past year, the focus of AI in education has shifted from generating content to evaluating its quality. While large language models can now produce vast amounts of material in seconds, ensuring that this content is accurate, reliable, and pedagogically sound remains a challenge. Emerging research shows that using AI as an evaluator is still unreliable, making human judgment more essential than ever. In this new paradigm, the real bottleneck is no longer creation but validation.

As artificial intelligence becomes deeply embedded in communication networks, software architectures, and industrial systems, AI-Native has fundamentally changed the system design, operation, and governance. Despite its growing influence, the concept of AI-Native remains ambiguously defined across domains, creating cognitive fragmentation and regulatory uncertainty. To harmonize different understandings and avoid confusions, this study develops a conceptual system and maturity evolution framework for AI-Native from an international standardization perspective, offering a structured foundation for both theoretical clarification and practical governance.

A recent study explored how scholars in the digital humanities research domain are navigating this new and complex landscape. Digital humanities is an interdisciplinary research field where scholars employ digital tools and computational methods to investigate cultural and humanities questions. Drawing on an international survey of 76 respondents and 15 in-depth interviews, the study found that scholars are not simply embracing or rejecting these tools. Instead, they are adopting AI systems cautiously, using them to speed up routine tasks, explore ideas, and build new skills, while navigating problems of accuracy, authorship, and what these systems might mean for the future of scholarship. The big question is no longer just whether AI is impressive, but whether it is becoming a genuine research partner, a useful tool, or, for some, still more of a “dancing bear” than a trusted collaborator. By tracing these mixed reactions and everyday practices, the study offers a grounded look at how AI is beginning to reshape academic life.

Generative AI (GenAI) has rapidly become a common source of advice in high-stakes domains such as healthcare and in everyday decision-making. Yet their black box nature often leaves users uncertain about how outputs are produced and whether they should be trusted. Explainable Artificial Intelligence (XAI) is widely viewed as a potential remedy. However, research and recent debates suggest an important tension: adding explanations does not always lead to better outcomes. This study addresses a central question for research on human–AI interaction and information science: When do explanations facilitate information adoption, and when do they hinder it?

Misinformation poses a significant threat to the knowledge economy, undermining trust, distorting markets, hindering innovation and eroding the credibility of scientific research. The knowledge economy relies on the free flow of credible information, but misinformation and disinformation can disrupt this process, leading to measurable welfare losses. We conducted a semi-systematic literature review of 11 scientific articles on misinformation and knowledge economy. Findings reveal that misinformation has negative impacts on: epistemic trust, markets and productivity, innovation systems, human capital formation, and higher education. Furthermore, academic libraries play a crucial role in mitigating these effects by promoting information literacy, defending their role as trustworthy intermediaries, and collaborating with other knowledge-producing institutions. By recognizing libraries as core infrastructures of the knowledge economy, we can work towards sustaining the integrity and productivity of the global knowledge economy. This study concludes by highlighting the role of academic libraries in promoting information literacy and combating misinformation, particularly in emerging knowledge economies.