Comprehensive Empirical Dataset: 600+ Pages of AI-Resistant Performance Tasks Designed by Teachers via IAT Model (Volume 1: Task Design Artifacts)

This extensive dataset serves as the primary empirical evidence for the "Task Design" module of the Integrated AI Triade (IAT) model. It contains over 600 pages of instructional artifacts generated by diverse cohorts of teachers during four separate professional development courses held in 2024-2025. Dataset Scope: The document compiles hundreds of unique "AI-Resistant" performance tasks designed by novice educators who utilized Generative AI as a "Design Partner." The sheer volume of this dataset demonstrates the scalability and reproducibility of the IAT framework across different subjects and grade levels. Structure of the Compendium: The dataset is organized into three distinct pedagogical patterns: 1. Scenario-Based Tasks: Real-world problem-solving simulations. 2. Comparative Analysis Tasks: Critical evaluation of AI-generated conflicting viewpoints. 3. Creation & Critique Tasks: Higher-order thinking exercises involving the refinement of AI outputs. Methodology: These artifacts were produced using the specific prompt engineering protocols detailed in the IAT Technical Report. All personal data has been anonymized for ethical compliance.