This is the conclusion I’ve come to after witnessing the growing popularity of AI tools. AI is everywhere, despite its propensity to produce misleading or incorrect information, and the moral objections of workers who fear losing their livelihoods. Why do patrons choose AI tools while more reliable (and less controversial) options sit on the shelf?
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Among the most recent AI developments with the potential to significantly transform library services is tiny machine learning (TinyML). TinyML is a low-latency technology that enables intelligent, on-device decision-making without requiring data transmission to the cloud for processing.
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From Page to Stage: Grand Finale was held on Friday, 23 January 2026, at the Multi-Purpose Building of the Faculty of Engineering, University of Moratuwa, as part of the Pre-Academic Term (PAT) programme. The event transformed a technical academic space into a living stage where stories, emotions, and student voices took centre place, showing how literature can shape confidence, empathy, and communication alongside formal academic learning.
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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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The path to earning a Doctor of Philosophy (PhD) or Master of Philosophy (MPhil) degree in Nigerian universities is a carefully structured journey, marked by a series of academic milestones that ensure students are thoroughly prepared for independent research and academic excellence.
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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.
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Artificial intelligence, specifically Generative AI, is a topic that cannot be ignored in education, regardless of the level. As a library director at a four-year private university, I believe it is our duty as librarians to meet the challenge of AI head-on and meet the instructional needs AI creates. Using generative AI is a skill that both students and faculty should be trained in, including the proper uses of AI and how it can be used as a tool, while also being clear how to use it in a positive way. Libraries are uniquely positioned to do oversee this effort on college campuses, because AI literacy is information literacy, and librarians should lead.
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One of the main challenges which academic librarians face when trying to develop programs and services that support AI literacy is the wide array of stances taken by institutions, and individual faculty members, when it comes to teaching with and about AI tools. These dimensions are not only applicable to students, but also to faculty, who also need help and guidance navigating the new technologies. One aspect which becomes central to this conversation is promoting the ethical use of AI tools in the academic environment. Although the topic has gathered considerable attention in recent conversations, these remain fragmented and divisive.
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The pace of adoption of generative AI has been groundbreaking—faster than the adoption of personal computers and the internet. Advocates argue that AI can help bridge digital literacy barriers and provide non-experts with access to specialized information, from coding assistance to digestible legal and medical information. It supports learning across the educational spectrum, from K-12 through higher education and workplace training. While we must accept that students will inevitably find and use generative AI tools, two critical issues demand attention: first, disparities in AI acceptance and use among students and faculty could deepen existing digital divides and affect educational and career outcomes; second, within higher education institutions, questions remain about whose responsibility it is to teach students how to use AI tools effectively and ethically.
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AI-generated images and videos are now frequently found across social media, advertising, and academic spaces, yet many users interact with these visuals without recognizing them as AI or understanding how they are created. As academic libraries increasingly position themselves as leaders in information and digital literacy, AI image literacy presents both a challenge and an opportunity. To help our patrons better understand, I developed and facilitated an AI image literacy workshop focused on helping participants critically evaluate AI-generated images and videos while also understanding how they are made.
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