Prompting AI models to contradict your ideas can improve your thinking about a problem.
Articles on Generative AI
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Big AI companies may already know much more about you than you’re comfortable with, even if you’ve never used their services.
Three years ago, researchers tested AI on real Australian law exams and it struggled. They wanted to know if this is still the case.
The US legal system often fails to provide counsel for millions of Americans in civil cases. Generic and law-specific AI models can help address this justice gap of unmet legal needs.
Canadians are turning to AI mental health chatbots for support. But what if the bot agrees with the beliefs that are keeping you unwell?
Trusting AI too blindly could end up costing you money – and see your legal case dismissed.
AI companies are turning to advertising to fund expensive systems. But commercial incentives could influence how answers are generated.
Canada’s current framework treats deepfake-based sexual abuse as isolated criminal behaviour, when it is systematic gender-based violence.
If checking each other’s messages for AI fingerprints becomes a habit, even casual communication may be marred with suspicion and doubt.
The very tool that generates disinformation and slop could also, when used well, be a catalyst for teaching youth to listen.
There’s no single giveaway. But there are patterns.
Beyond technical fluency, Canadian students need judgment to decide how to use AI and when it should not be used at all.
A survey and workshop project is designed to help educators reflect on their comfort with genAI, assessment and educational practices and their fears and challenges.
For older employees already experiencing burnout, poorly designed or poorly timed training can become an additional job demand rather than a solution.
As AI transforms graduate careers, universities should rethink when students specialise and how they combine disciplinary expertise with broader skills.
AI-powered breakthroughs are raising big questions about how to do mathematics – and why we do it in the first place.
Why AI companies are so fond of the prefix ‘co’.
Anyone can now build an app by describing an idea to AI. But software that appears to work isn’t always secure, reliable or ready for real-world use.
A standardized framework for regulating the safety and efficacy of AI in healthcare has yet to be established.
Universities’ crackdown on the use of AI misses the point. They should instead teach it as a vital productivity tool, undepinned by ethics.
Top contributors
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Associate Professor of Visual Communication & Digital Media, RMIT University
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Senior Lecturer in Law, University of New England
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Associate Professor of Linguistics and Director of Language Lab, The University of Western Australia
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Senior Lecturer in Law and Emerging Technologies, Western Sydney University
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Senior Lecturer in Business Information Systems, University of Sydney
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Professor of Philosophy and Director of Applied Ethics Center, UMass Boston
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Professor in Computational Intelligence, Australian Catholic University
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Adjunct Lecturer in Public Policy, Harvard Kennedy School; University of Toronto
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Professor of Digital Communication, Director of QUT Digital Media Research Centre, Queensland University of Technology
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Professor of Computer Science and Director of the Art & AI Lab, Rutgers University
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Director of Sydney Executive Plus, Business School, University of Sydney
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Professor of Computer Science, University of California, Berkeley
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Associate Professor of Information Science, University of Colorado Boulder
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Assistant Professor, Faculty of Business, University of New Brunswick
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Associate Professor of Political Science, Brock University