AI - Information Matters

Health information no longer moves in a neat, straight line from the researcher’s bench to the clinician’s desk to the patient. Today, it ricochets. It travels through search engine auto-completes, TikTok feeds, private WhatsApp groups, and AI-generated summaries long before a patient ever sits down with a doctor or a medical librarian. For cancer patients and survivors exploring Complementary and Alternative Medicine (CAM), this networked reality creates both real possibility and serious risk. In digital spaces, the challenge isn’t just finding information. It is untangling how certain health claims become highly visible, endlessly repeated, and ultimately trusted.

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April 30, 2026 Chengzhi Zhang

Can AI Really Understand Scientific Novelty? Insights from a New Benchmark

In academic research, novelty is one of the most important criteria for publication. A paper is expected to contribute something new, whether a method, a dataset, or a theoretical insight. But identifying novelty is not straightforward. Even experienced reviewers may disagree, and the rapid growth of scientific publications has made the task increasingly difficult. As the volume of submissions continues to rise, the peer review system faces growing pressure. This has sparked interest in whether artificial intelligence, particularly large language models (LLMs), can assist in evaluating research novelty. But before we can rely on AI for this task, a fundamental question must be answered: do LLMs actually understand novelty?

Original

April 28, 2026 Joseph Ryann Jalagat

AI as Co-Author of Error? Disinformation, Verification, and the Fragility of Knowledge Work

Despite AI’s leverage on knowledge production, management, and storage, this informational revolution introduces a paradox. The expansion of information availability also presents risks of destabilizing knowledge trust. The root of this all leads us to AI hallucination, which produces believable and sophisticated results but is actually contrived and fictitious. It is in this sense that AI not only serves as an aid, but as a co-author of error.

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April 7, 2026 Rongqian Ma

Expert Colleague or Dancing Bear? The Mixed Responses to AI in Digital Humanities Research

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.

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April 2, 2026 Data Analysis and Knowledge Discovery

Explanation Singularity of Explainable Artificial Intelligence (XAI): An AIGC Information Adoption Perspective

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?

Education Opinion

March 21, 2026 Azeez Adeoye

Digital Cocaine: The Business Model of AI Addiction, When the Savior Becomes the Dictator

When artificial intelligence systems were first introduced to the public around 2022, they were celebrated as revolutionary assistants, tools designed to augment human productivity, creativity, and efficiency. The early versions were freely accessible or offered generous trial capabilities. Students used them to summarize readings; professionals used them to draft emails; programmers relied on them to debug code. The public welcomed these tools with enthusiasm, regarding them as the next great step in technological progress. Yet by 2026, the situation has evolved in ways that invite deeper reflection.

Featured Original

March 11, 2026 Tova Harris

Who Gets to Train the Machine? AI, Race, and Storytelling in Library Makerspaces

As generative AI tools move into library makerspaces, they do more than draft metadata or translate text—they reflect the power structures embedded in the spaces where they are deployed. Without intentional design, we risk automating the very inequities libraries seek to dismantle.

Education Featured Original

March 3, 2026 Azeez Adeoye

The Forgotten Jewel of a Good Book: A Compass to Modern Discoveries such as the Internet, Search Engines, and Generative AI

Many have argued about the place of technology, computer systems and their paraphernalia such as e-books, audiobooks, and websites, whether they are a blessing or a curse. Nevertheless, the products of past civilisations, such as the discovery of paper and the invention of the movable printing press, books, and writing itself, remain the true success stories behind all modern emerging technologies.

Editorial

February 10, 2026 Chirag Shah

In Defense of (Human-led) Science

Doing science is not the same thing as producing science. Science, at its core, is a profoundly human activity. It involves not just generating knowledge but developing judgment about what questions matter—asking not only how something might be done, but whether it should be done at all.

Education

January 15, 2026 Dan Wu

Exploring the Future of Human–AI Collaboration: Insights from “Human–AI Interaction and Collaboration”

How should people and AI work together in ways that are useful, ethical, and trustworthy? Edited by Dan Wu and Shaobo Liang (Wuhan University), “Human–AI Interaction and Collaboration” maps the fast-moving terrain where users, systems, and information meet—treating human strengths and machine strengths as complements, not substitutes. The introduction frames collaboration as a user-centered endeavor that must balance capability with ethics, transparency, and trust.