At Queen's we understand AI literacy as the knowledge, skills and values to inform the critical judgment to engage with AI competently and responsibly, including making informed decisions about when, how, and whether to use it or not.
This definition matters because it draws a clear line between literacy and adoption. An AI-literate person is not necessarily an enthusiastic AI user. They are someone who understands enough about AI to make sound decisions about it: when it serves them, when it does not, and how to use it responsibly when they choose to. Literacy comes first. Informed use, where it occurs, follows naturally from understanding. This aligns with the insights of the conceptualizations of AI literacy from the Digital Education Council, Lo鈥檚 AI Literacy for All framework, and the AI Competency Framework for Teachers from UNESCO.
AI is not a minor technical update to how we work and learn. It is changing how knowledge is created, evaluated, and trusted. That is why building genuine AI literacy across the Queen's community is a long-term institutional priority, not a one-time training exercise.
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An AI-literate member of the Queen's community can explain, in accessible terms, how generative AI produces outputs, where those outputs come from, and why they cannot be accepted without scrutiny. They grasp foundational concepts such as machine learning, algorithms, training data, and pattern recognition, and they understand the role data quality plays in shaping system behaviour. They recognize the key limitations of AI, including bias in training data, hallucination, recycled or outdated knowledge, and the absence of genuine understanding or reasoning. This technical grounding requires only enough conceptual familiarity to move past surface impressions and engage meaningfully with how and why AI behaves as it does.
An AI-literate member of the Queen's community understands that AI systems do not exist in isolation: they are built by people, trained on human-generated data, and deployed within social contexts that carry ethical weight. They can identify core ethical concerns attached to AI use, including bias and fairness, transparency, accountability, intellectual property, data privacy, and consent. They are alert to the values and assumptions embedded in AI tools and to the ways those tools can reproduce or amplify existing inequities. They know where to find and how to apply Queen's policies and guidelines on responsible AI use across teaching, learning, research, and administration, and they can navigate the ethical questions that arise when those policies meet real situations.
An AI-literate member of the Queen's community can evaluate AI-generated content for accuracy, bias, completeness, and appropriateness in context. They extend established practices of information literacy to AI, probing not only the outputs of a system but also the sources, data, and assumptions that shape it: whose voices are represented, whose are absent, and whose interests the system serves. They approach AI with informed skepticism rather than either uncritical trust or blanket dismissal, recognizing that these tools are neither infallible nor neutral. They can synthesize multiple perspectives when assessing AI claims, and they use critical engagement to retain their own agency and judgment in a landscape increasingly shaped by automated outputs.
An AI-literate member of the Queen's community can use AI tools purposefully for tasks relevant to their learning, teaching, research, or professional role. They can design prompts that produce useful outputs, adapt those outputs for specific needs, and integrate AI into their workflows where it genuinely adds value. They can compare AI-assisted approaches with unassisted ones and judge which is appropriate for the task at hand. They use AI in ways that support their own thinking rather than substitute for it, and they adapt their practice to the specific norms and expectations of their discipline or role. They also recognize that practical skill includes knowing when not to use AI: when human judgment, original effort, or direct human engagement better serves the goal.
An AI-literate member of the Queen's community understands that AI is reshaping economies, cultures, institutions, and ecosystems, and that individual choices about AI use aggregate into societal patterns. They can identify the broader costs and concerns attached to AI, including environmental impact and resource consumption, labour displacement, surveillance and erosion of privacy, the concentration of power among a small number of developers, and the potential for gradual erosion of human expertise, namely skill atrophy, never skilling, and de-skilling at population, organizational, and individual levels. They also recognize AI's potential to support positive change when deployed thoughtfully. They can make context-sensitive decisions that reflect their own values, the expectations of their community, and Queen's principles of responsible practice. They apply disclosure norms honestly and consistently, and they understand that being AI-literate is, ultimately, about participating responsibly in shaping how these technologies enter our shared life.