In this module, you'll learn to teach the AI Fluency Framework rather than just apply it. You'll start by selecting from four distinct teaching approaches (linear, non-linear, focused, and two-loops) and adapting them to fit your students and context. You'll then go deep on each of the framework's two loops as teachable units: the Delegation-Diligence loop for strategic and ethical decision-making about AI use, and the Description-Discernment loop for the moment-to-moment craft of building productive cognitive environments with AI. By the end, you'll have concrete strategies for helping students see how the loops interact in real collaboration.

In this module, you'll learn how to evaluate student AI Fluency in ways that capture genuine skill development. You'll work with three complementary assessment approaches: outcome-based (what students produce), process-based (how they work with AI over time), and reflection-based (their metacognitive awareness), and you'll see how to apply each across the 4D competencies. You'll also learn to design assignments that build authenticity, iteration, and pedagogical transparency into the work itself, plus practical strategies for managing the increased volume of AI-enhanced student output through rubrics, peer review, and selective sampling.

In this module, you'll examine how AI is reshaping your specific field across curriculum, pedagogy, and assessment, and apply your disciplinary expertise to make the 4D Framework field-specific. You'll analyze what gets automated in your discipline, where human-AI partnership adds the most value, and how to prepare students to manage and stay accountable for AI systems in their future careers. You'll then work through how to make tacit disciplinary knowledge explicit by defining quality criteria for Discernment, mapping communication norms for Description, mapping work decomposition for Delegation, and codifying ethical standards for Diligence — ideally in collaboration with colleagues in your department.