AI - Information Matters

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.

Original

January 12, 2026 Christopher Lueg

GenAI Benefits and GenAI Burdens: Two Sides of the Same Coin?

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.

Translation

December 19, 2025 Evelyn Cox

AI in a Tribal Context: Diverse Perspectives Matter in a Changing Landscape

With Artificial Intelligence’s (AI) seemingly increasing integration into various aspects of society, nations worldwide—including Tribal Nations—are assessing its impact on the changing landscape. AI is a revolutionary technology that poses potential opportunities and risks for federally recognized Indian Tribes (Tribal Nations or Tribes) and their citizens. This article provides an overview of the literature related to AI in a tribal context.

Education

December 18, 2025 Breanne Kirsch

Preparing Librarians for the Generative AI Age

In this paper, we explore successful AI training initiatives, resources and networks for librarians, AI integration strategies, and implications of AI for library services. Join me in discovering best practices for preparing librarians as leaders in the generative AI era and integrating AI into library services.

Editorial

December 13, 2025 Chirag Shah

Agentic Algorithmic Amplification and The Choices We Face

Whether we like it or not, know it or not, we have been living in the agentic age of algorithms long before the recent rush to building agentic systems. None of these systems were explicitly programmed to promote conspiracy theories. But they were programmed to maximize engagement, and they discovered—through the same machine learning techniques that help them recognize faces or translate languages—that false, emotionally charged content was engagement gold.

Opinion

December 4, 2025 Pathum B Rathnayake

Co-Pilot, Not Autopilot: Navigating the Future of Qualitative Research

Recently, a group of 3 published an open letter signed by over 400 qualitative researchers. Their message was clear: “Keep AI away from our work.” They argue that using AI to find themes in data kills the “reflexive” process, the deeply human act of interpreting nuance, emotion, and meaning. I have spent my career championing the human touch in the EdTech works and research. I understand their fear. However, I believe this total rejection is a mistake. We should not be banning Gen AI; we should be teaching researchers how to master it.