Imagine global politics not as a grand marble hall full of flags and translators but as a login screen. Username. Password. Payment tier selected. That’s the thought experiment at the heart of Trump’s Board of Peace. The idea here isn’t about whether it’s good or bad; it’s about what happens when international legitimacy starts to look and feel like a software product.
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The rapid development of digital technologies is reshaping humanities research and the preservation of cultural heritage worldwide. In response to these transformations, the book series Digital Humanities and Intelligent Computing has been initiated by the Intelligent Computing Laboratory for Cultural Heritage (ICLCH) at Wuhan University, originating from the Centre for Digital Humanities at the same institution, and is under contract with Routledge. The series is designed to provide a comprehensive global perspective on the latest advancements and trends in digital humanities (DH) and intelligent computing for cultural heritage, and to promote cross-cultural dialogue, knowledge exchange, and interdisciplinary collaboration.
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Open Access (OA) publishing has become a major change in how research is shared. In traditional publishing, readers pay to access articles through journal subscriptions. In OA, articles are free to read online right away. This trend has grown fast because many believe it makes science better and fairer. Supporters say OA articles get more citations because more people can read them, leading to greater impact. However, authors often pay high Article Processing Charges (APCs) to publish in OA journals. This creates a debate: Does OA truly boost research impact, or does it create new problems by charging authors large fees?
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Have you ever been excited to learn something new, but when you started reading about it, you felt completely lost and ended up setting it aside? A large volume of knowledge production is carried out by researchers, who typically convey it using the specialized language of their subject field. This often includes specialized terminology and expressions, acronyms or other abbreviated forms, stylistic conventions, and even particular ways of structuring or formatting a text. The end result is comprehensible to other researchers in the same subject field, but it may be far less accessible to other groups.
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If an economy were a body and its policies were cells, then the rumoured “Formosan flu” afflicting Taiwan might be best diagnosed not with a stethoscope but with an information scanner. In The Economist’s recent cover story (which dubbed the situation “Taiwanese disease” or “Formosan flu”) it was the persistent undervaluation of the New Taiwan dollar that served as symptom and signal. The magazine’s use of its own Big Mac Index, an informal gauge of purchasing power parity that suggests Taiwan’s currency is around 55 % undervalued relative to the US dollar, turned what might seem like a quirky economic snack into a sprawling public health narrative of economic strain. But what if this isn’t simply about price mechanics or exchange rates? What if Taiwan’s central bank isn’t just a macroeconomic organ but an information gatekeeper, curating the very signals that shape how the domestic body hears itself, thinks about itself and behaves?
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Human–AI Interaction and Collaboration, co-edited by Professors Dan Wu and Shaobo Liang of the School of Information Management, Wuhan University, arrives as a timely and deeply thoughtful contribution. Published by Cambridge University Press, this volume brings together twelve chapters by interdisciplinary scholars from around the world, offering a richly layered exploration of how humans and AI systems interact, collaborate, and co-evolve in contemporary sociotechnical contexts.
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In this special issue we explore the role that libraries, librarians, and information professionals can play in advancing AI literacy in our workplaces and communities. AI literacy is a broad term meant to encompass educating users about AI use, production, and evaluation; however, as we can see there is no commonly agreed upon definition as of yet. The included literature reflects common similarities in the need to incorporate AI literacy into our ongoing work as librarians and educators, while also recognizing that libraries serving such a wide base of communities will need to lead literacy efforts that are uniquely tailored to the populations they serve.
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Users of GenAI applications need to understand that using GenAI can lead to benefits but also introduces burdens. The benefits and the burdens are two sides of the same coin.
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Librarians have spent decades teaching students to evaluate sources, trace authority, and recognize how information is constructed. We’ve built sophisticated frameworks for this work. The advent of generative AI has obviously intensified these efforts. We’ve provided frameworks for evaluating AI-generated content: Is this output accurate? What biases might it contain? But as we’ve rushed to develop “AI literacy” programming, I wonder if we’ve overlooked a rich space where these same frameworks can be applied: the language we use to describe these systems in our own conversations, instructional materials, task forces, and workshops.
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The article argues that skilled AI prompting and academic libraries can work together to widen access to scholarly knowledge. As open access grows, AI can explain and synthesize openly available research for broader audiences. But paywalls still limit what AI can analyze, creating visibility gaps between open and closed scholarship. Libraries help address this by improving institutional repositories, supporting OA publishing through transformative agreements, teaching AI literacy, and developing ethical guidelines. Combined, these efforts make research more accessible, understandable, and useful worldwide.
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