Translation - Information Matters

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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June 3, 2026 Camilla Lyckblad

The Chatbot Will See You Now: When Access Isn’t Enough

Digital healthcare can increase convenience, reduce travel time, and make services available to more people, more quickly. Patients can access test results, book appointments, renew prescriptions, and, in many countries, read their medical records online. Yet as healthcare becomes increasingly digital, I find myself asking a different question: who is this system really designed for? More specifically, who is expected to understand it?

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May 24, 2026 Bo Hyun Hong

When AI Output Becomes “Good Enough”: Not Everyone Evaluates AI the Same Way

Even when people use the same AI system, they do not evaluate AI-generated information in the same way. For example, imagine two students using Gemini or other generative AI tools for the same assignment and both receive nearly identical answers. One student quickly accepts the response and moves on. The other pauses, checks the information against outside sources, and revises the AI-generated output before using it.

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May 23, 2026 Shagun Jhaver

Users’ Perspectives on Content Moderation of Web Search Autocomplete Suggestions

The prospect of moderating Autocomplete suggestions raises a range of ethical, technical, and political questions, such as how to distinguish between appropriate and inappropriate suggestions, who should have the power to make such distinctions, and how these decisions should be communicated to end-users. This article takes a user-centered approach to interrogating these questions. By conducting semi-structured interviews with 20 regular users of search platforms, I examine how users make sense of Autocomplete moderation, what concerns they have about its procedures, and how they seek to assert greater agency within the process.

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May 7, 2026 Güleda Doğan

Universities Are Judged by Rankings. But Who Judges the Rankings?

Global university rankings have become one of the most influential tools in higher education. Governments use them to shape internationalization policies, universities use them for strategic planning and reputation management, and students often rely on them when making educational choices. In many countries, rankings are no longer simply reference tools. They increasingly function as policy instruments. But an important question is often overlooked: who evaluates the rankings themselves?

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May 7, 2026 Tim Zijlstra

Opposite ends of the Tay: Collaboration between the NHS and Public Libraries in Tayside (Scotland)

Opposite Ends of the Tay explores a growing collaboration between NHS Tayside and public libraries to strengthen information literacy and support preventative, person‑centred healthcare. Set against stark health inequalities in Scotland, it argues that libraries are vital community infrastructure for enabling people to find, judge and use health information safely.

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May 5, 2026 Jinglong Chen

Why Slow Journal Decisions Hurt More Than We Think

Anyone who has submitted a research paper knows the feeling. After months or years of work, the manuscript disappears into the peer review system. Days become weeks, weeks become months. You keep checking the submission portal, waiting for an answer. The final outcome certainly matters. Acceptance brings relief, rejection brings disappointment. But our study finds that the wait itself also matters, and more than we usually admit.

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May 1, 2026 Henna Bhramdat

Can AI Describe Art as We Do? A Case Study on a Pottery Collection

There are two capabilities of current large language models (LLM)-based AI systems that we attempt to evaluate for improving the discoverability of library and museum collections, which are often searched by using expert-defined keyword vocabularies through complex hierarchical categories: 1) Vector search: differing from the traditional keyword search, it improves discovery of word semantic relationships in a broader natural language domain and 2) Multimodal large language models (MLLM): combining computer vision processing images alongside LLMs, boosting understanding of the image both textually and visually. We explore how visual language models (VLM) and MLLMs can bridge vocabulary gaps in search between expert-generated descriptions and the public.

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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?

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April 25, 2026 Nove E Variant Anna

When You Message a Librarian at 11 PM: How Virtual Help Became Real Learning

Many people view librarians as the people who help you find a book, answer a quick question, or point you to the right database. But in today’s academic environment, where students learn online, write theses with digital tools, and face intense pressure to publish, librarians often do something much bigger: they teach.