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AI literacy

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More than prompting, more than ethics

AI literacy cannot be reduced to technical knowledge, operational skill, ethical awareness, or critical thinking alone. It requires integration across six dimensions, with particular emphasis on judgement and metacognition because AI engagement is inherently contextual and rapidly evolving.

AI literacy is the multidimensional capability to recognise AI systems, critically evaluate their outputs and limitations, use them effectively for appropriate purposes, create meaningful outcomes through collaboration with AI, understand the ethical implications of AI engagement, and develop contextual judgement about when and how AI serves professional goals.

This definition applies a common architecture of literacy to artificial intelligence, recognising that AI literacy cannot be reduced to any single dimension:

  • Technical knowledge alone (understanding how LLMs work)
  • Operational skill alone (knowing how to write prompts)
  • Ethical awareness alone (recognising bias and limitations)
  • Critical thinking alone (evaluating AI outputs)

Rather, AI literacy requires integration across six dimensions, with particular emphasis on judgement and metacognition because AI engagement is inherently contextual and rapidly evolving.

The six dimensions of AI literacy

1. Access and recognition

Understanding what AI systems are and recognising when they are present and relevant. This includes distinguishing AI from other technologies, understanding AI as a language-based cognitive extension rather than a tool, search engine, or database, and identifying appropriate contexts for engagement. An AI-literate person knows when AI might help, when it is likely to hinder, and when it is simply irrelevant.

2. Critical evaluation

The capacity to assess AI outputs for quality, accuracy, and reliability. This involves understanding that AI systems can hallucinate, recognising the difference between fluent and accurate responses, identifying bias in outputs, and understanding complementary errors—where humans and AI make different kinds of mistakes. An AI-literate person does not accept AI outputs uncritically but maintains appropriate scepticism while remaining open to genuine assistance.

3. Functional application

The practical ability to use AI systems effectively for specific purposes. This includes operational competence with prompting, understanding how to structure requests for better outputs, knowing which AI tools suit which tasks, and deploying AI capabilities to achieve goals across domains like research, writing, teaching, and administration. An AI-literate person can get useful work done with AI systems.

4. Creation and communication

The skill to generate meaningful new outputs through collaboration with AI. This moves beyond consumption to production—using AI to develop arguments, decompose complex problems, generate research questions, produce scholarly outputs, and communicate ideas. An AI-literate person creates through partnership with AI rather than merely receiving its outputs.

5. Ethical awareness and responsibility

Understanding the social, ethical, and professional implications of AI engagement. This includes recognising issues of transparency and attribution, understanding when AI use might undermine rather than support goals, maintaining scholarly voice and integrity, avoiding over-reliance, and considering how AI engagement affects others. An AI-literate person makes responsible choices about when and how to use AI.

6. Contextual judgement and metacognition

The development of taste and professional judgement about meaningful AI engagement. This involves distinguishing genuine competence from mere familiarity, recognising when AI helps versus hinders, evaluating both outputs and processes, and developing domain-specific judgement that evolves with experience. An AI-literate person knows not just how to use AI but when its use is appropriate and valuable.

AI literacy is developmental

AI literacy is not binary—literate or illiterate—but developmental. People begin where they are and progress through increasing sophistication. Additionally, AI literacy is domain-specific. What constitutes meaningful AI engagement differs across contexts. Someone can be highly AI-literate in one domain while still developing literacy in another.

The research literature confirms this multidimensional view. Long and Magerko’s foundational 2020 framework identifies 17 competencies spanning recognition, understanding, evaluation, and critical engagement. UNESCO’s 2024 framework emphasises knowledge, skills, and attitudes working together. Recent frameworks also increasingly stress that AI literacy involves moving from consumer to interpreter to collaborator—a progression that requires developing all six dimensions.

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