This session develops our capacity to work through standards thoughtfully before turning to AI for support. We will learn how to analyze the language, cognitive demand, and embedded expectations of a standard to craft learning intentions, success criteria, questions, and tasks that truly reflect the rigor. We’ll examine why relying on GPT to generate LI/SC without deep understanding leads to lowered expectations. Together, let's practice answering our own clarity questions and tasks first, ensuring we can model, scaffold, and support students toward high expectations.
This session focuses on strengthening teacher expertise in unpacking standards, designing high‑quality questions and tasks, and using AI as an enhancer—not a substitute—for professional judgment. We will learn how to determine the true rigor of a standard, identify the thinking students must do, and create scaffolds that move all learners toward ambitious outcomes. We’ll confront the tendency to unintentionally lower the bar. By developing the skill to answer tasks themselves, teachers build clarity, confidence, and the capacity to hold students to high expectations.
This session centers on designing scaffolds that lift students up to the rigor of the standards rather than protecting them from it. We will learn how to analyze the cognitive demand of a task, identify the precise skills students need, and build supports that maintain—not dilute—high expectations. We’ll examine how intentional scaffolding helps every learner reach ambitious goals. We will practice modeling thinking, anticipating misconceptions, and creating step‑by‑step pathways that gradually release responsibility while ensuring all students can access complex learning.
