🏷️ Concept
AI Literacy
AI literacy — the knowledge, skills, and critical dispositions needed to understand, evaluate, and effectively use AI technologies in educational contexts. AI literacy spans foundational understanding of how AI works, practical competence in using AI tools, critical evaluation of AI outputs, and ethical awareness of AI's societal implications.
AI literacy has rapidly emerged as a core competency for learners, educators, and institutions as generative AI becomes embedded in education. Unlike general digital literacy, AI literacy requires understanding probabilistic systems that can hallucinate, exhibit bias, and shift agency from human to machine — making Critical Thinking and Over Reliance central to the construct.
Dimensions of AI literacy
AI literacy research in this wiki spans four interconnected dimensions:
Foundational knowledge: Understanding what LLMs are, how they differ from rule-based systems, and their fundamental limitations. This includes awareness of model capabilities, training data biases, and the distinction between task-specific AI and general-purpose models. Research in Prompt Engineering examines how understanding prompt mechanisms affects effective AI use.
Practical competence: The ability to use AI tools effectively — from Prompt Engineering to interpreting outputs. Studies of student GenAI usage patterns reveal that tool access alone doesn't produce competence; structured practice and Scaffolding are essential. The vibe coding framework shows how K-12 teachers can develop practical AI literacy through guided tool creation.
Critical evaluation: The capacity to assess AI outputs for accuracy, bias, and appropriateness. Research on literacy assessment shows a 40% gap between self-reported and performance-based AI literacy — people consistently overestimate their evaluation skills. This connects to Over Reliance research showing that students who trust AI uncritically learn less.
Ethical and institutional awareness: Understanding AI's broader implications — from Academic Integrity to Equity to Privacy. AI literacy at the institutional level involves policy development, Faculty Development, and governance frameworks. The EPIQ-AI framework frames institutional AI literacy as a sociotechnical alignment challenge, not just individual training.
How AI literacy is developed
Research points to collaborative and active approaches as most effective. The ICAP framework (Passive → Active → Constructive → Interactive) provides a useful progression: students learn AI literacy best when they co-construct knowledge rather than passively receive information. Practical activities — designing prompts, evaluating outputs in groups, debating AI ethics — outperform lectures.
Connections across the wiki
AI literacy intersects with AI Tutoring (understanding when and how AI tutors are effective), Teacher AI Competency (educator preparedness), Academic Integrity (knowing what constitutes appropriate AI use), and AI Education broadly. It is both a prerequisite for effective AI use and an outcome of well-designed AI integration — students learn AI literacy BY using AI critically, not just by learning ABOUT AI.