Every day we ask AI to draft, summarize, and explain. But when does a chatbot’s answer become a document you can trust? Drawing on a conversation with ChatGPT, Sascha Donner shows how humans confer the trust, force, and answerability that turn an AI answer into an accountable document.
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What happens when students can write a literature review, analyze information, and receive instant feedback with a few prompts? What happens when they make a summary of literatures and answers questions based on the documents they upload? What happens when they find relevant questions and answers for discussion in their thesis work using Grok? Yes, these are possible and researchers are using these tools in effective and productive ways.
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There is something brutal about the title of a recent Spectator article: “We pretend to teach, they pretend to learn”. The article describes the author’s experience teaching Chinese master’s students in a UK university, particularly students with limited English, low classroom participation and little apparent interest in engaging with the course beyond getting the degree. It is deliberately provocative, occasionally crude and very easy to dismiss as yet another piece of anti-Chinese-student polemic. But dismissing it entirely would be too comfortable.
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R. David Lankes has written here and posted a preprint, submitted to the Journal of Documentation, arguing that generative AI is causing an “Information Shock”: a disruption so deep that it dissolves the document — the field’s organizing unit — and demands a new information science. It is the best account we have of what generative AI does to the documentary foundations of information science, and I agree with nearly all of it. The distinction between AI that produces document-like objects and AI that co-authors exchanges is the sharpest framing of the problem in our literature. The argument that AI literacy carries document-evaluation over intact and aims it at something that is not a document is correct and overdue. What follows is not a rebuttal but an extension — because the most important sentence in his argument is nearly its last, and it stops one step short of a tradition built for exactly this moment.
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We are living in the early stages of an Information Shock caused by generative AI. Information Shocks are rare, but historically transformative. This one will require a reinvention of information science itself.
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Have you ever used strategies while searching for a specific problem? Well, most youngsters do not even bother whether they are consuming actual information on the internet or not, because they only care about synthesized information to fulfill their needs. The major issue in developing nations like Pakistan is that we, as students, face the risk of believing all the information we find on Google or social media is accurate. When it comes to information literacy, people who consume content on social media and in their daily lives don’t bother about the authentication of the news they consume. So, the question is what will happen when students get literacy instructions at college and early university life in all disciplines?
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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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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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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.
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We must remember that those who profit the most from our growing reliance of GenAI are the tech companies themselves. Meanwhile, the people who are the most excited about AI are the ones who understand it the least. While machine learning can be useful, I argue that GenAI comes at an unacceptable cost. Taking in to consideration GenAI’s role in the spread of disinformation, the complex damages caused to people and the planet along with the proven negative effect to cognitive skills among users, this text advocates for critical perspectives, and ideally, critical refusal of GenAI.
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