AI Fluency: Framework & Foundations

This 12 part open course provides an introduction to the AI Fluency Framework (by Rick Dakan and Joseph Feller) and lays a foundation for developing the four competencies of Delegation, Description, Discernment, and Diligence, as a flexible, technology-independent approach to human-AI interaction that is effective, efficient, ethical and safe.

There are approx. 70 mins of video lessons, numerous (ungraded) practical exercises & projects, and reference handouts.

The course is suitable for all learners curious about AI. In particular, it can be used by educators, academic developers, instructional designers, and others to build personal AI Fluency and apply it in their professional practice, and also to share directly with students to help them build AI Fluency and apply it in their academic setting. The AI Fluency Framework is both a useful descriptive framework for critically discussing human-AI interaction and an applied normative framework for skill-building. The course exercises can be adapted for formative and summative assessment. The summary handouts are conveniently presented in slide-friendly form for building presentations.

• Recognise and apply the AI Fluency Framework terminology and concepts, particularly the four core competencies (Delegation, Description, Discernment, and Diligence) across three modes of interaction (Automation, Augmentation, and Agency);
• Understand the technical foundations of generative AI systems, including generative AI fundamentals, model capabilities and limitations, how large language models are trained, their key characteristics, etc;
• Evaluate when and how to delegate tasks effectively between humans and AI systems, considering platform capabilities, task requirements, and the strategic distribution of cognitive work based on understanding of AI strengths and limitations;
• Design and implement effective communication strategies with AI systems, applying proven prompting techniques, contextual guidance, and iterative refinement approaches to enhance collaborative outcomes;
• Critically assess AI outputs, processes, and behaviours using systematic discernment approaches to identify quality issues, factual errors, and appropriateness in educational and professional contexts;
• Apply ethical and responsible AI practices, including transparency standards, accountability measures, and diligent verification of AI-assisted work products while understanding the broader implications of AI integration in higher education;
• Develop personalised AI fluency development plans and policies, including competency self-assessment, prompt libraries, ethical guidelines, and context-specific standards for AI integration in teaching, learning, and professional practice.