What are the three key principles for designing assignments that assess AI Fluency?	Authenticity, iteration, and pedagogical transparency.
What is the primary function of a process-based assignment in assessing AI Fluency?	To make invisible decision-making visible through documentation (e.g., annotated chat logs).
How do reflection-based assignments contribute to student learning?	They develop metacognitive awareness (the ability to think about one's own thinking).
What do outcome-based assignments primarily focus on demonstrating?	The final products, while also revealing the student's collaboration skills with AI.
In the context of AI Fluency, what does "pedagogical transparency" require?	Being clear about assessing the collaboration process, not just the final output.
What is the purpose of building in "iteration" into an assignment design?	To create opportunities for refinement and showcase the student's growth over time.
Name three practical strategies for managing the increased volume of AI-enhanced student work.	Using detailed rubrics, emphasizing peer review, or selective sampling of work.
When designing an assignment, what must the components do to ensure the workload is manageable and relevant?	They must be adapted to feel natural within the course context and mirror real-world collaboration.
What is the core goal when designing an AI Fluency assignment architecture?	To ensure the assignment mirrors real-world AI collaboration and builds in genuine problems where AI partnership adds value.
When assessing AI Fluency, what should instructors explicitly state they are assessing?	The process and reflection, not solely the final output.