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EditorialFeatured
Chirag Shah

Ship It, Then Apologize: We Can Do Better Than This for AI Advancements

Three days. That’s how long Fable 5 lasted before the U.S. government ordered Anthropic to switch it off worldwide, citing a vaguely described “jailbreak” and an export control directive broad enough to sweep in Anthropic’s own employees abroad. But the recall is only half the story: Anthropic had also moved fast, pushing its most capable model to the public within months of keeping its predecessor restricted to vetted partners. From Gemini’s image generator to Tay to GPT-4o’s sycophancy rollback, this is a pattern we keep repeating, and the people who pay for it are never the ones who decided to ship.

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Translation
Tara Whitson

Trust and Generative AI in Organizational Communication and Culture

Written communication promotes understanding, conveys authority, and inspires action. In organizations, emails often serve as a proxy for leadership presence by setting a tone, articulating expectations, and reinforcing organizational culture. Increasingly, however, this communication is no longer written solely by the sender. With a simple prompt, generative AI can instantly produce a confident response before we even have time to think or take a breath. These tools are now being suggested, and in some cases required, for drafting organizational communication. For the sender, AI-assisted writing can be helpful and even a source of relief when balancing tasks. For the recipient, AI-assisted writing may appear professional and well written. Over time, however, recipients may learn to recognize the patterns and tone of AI-generated communication. This recognition may change how people interpret the intent and authenticity of written communication.

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Opinion
Elaine Kong

When Health Claims Travel Faster Than Evidence: CAM Information in Networked Spaces

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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EducationFeatured
Azeez Adeoye

The Art of Scholarly Research in the Era of Artificial Intelligence: Assessing, Organising, and Using Academic Literature

Research is a lifelong intellectual endeavour that transcends academic qualifications, professional status, and social background. Whether one is an undergraduate student, postgraduate scholar, healthcare practitioner, policymaker, entrepreneur, volunteer, or independent learner, research remains indispensable to growth, innovation, and societal advancement. Indeed, every meaningful improvement in human endeavour is rooted in the ability to seek, evaluate, and apply credible knowledge. Research, therefore, is not merely an academic requirement; it is a systematic and continuous process of building upon existing knowledge to solve emerging challenges and expand the frontiers of understanding.

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Translation
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?

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Original
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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Translation
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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Translation
Jinao Zhang

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?

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EducationOpinion
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.

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