Comprehensive Empirical Dataset: 120+ Pages of AI-Automated Assessment Artifacts (Volume 3: Question Design, OCR, and Feedback Automation via IAT Model)

This dataset serves as the primary empirical evidence for the "Assessment Automation" module of the Integrated AI Triade (IAT) model. It compiles over 120 pages of instructional artifacts generated by teachers who utilized Generative AI to streamline the evaluation workflow, shifting from manual correction to strategic analysis. Dataset Scope: The document demonstrates the practical application of AI in reducing teacher workload while enhancing feedback quality. It includes unique examples of bridging the analog-digital gap using AI vision tools. Structure of the Compendium: The artifacts are organized according to the IAT Assessment Protocols (Session 3): 1. Smart Question Design: AI-generated questions tailored to specific cognitive levels (Bloom's Taxonomy). 2. Automated Quiz Generation: Rapid creation of structured quizzes and answer keys. 3. Digitization (OCR) Artifacts: Real-world examples of converting students' handwritten assignments into digital text for AI analysis. 4. AI-Generated Descriptive Feedback: Personalized, evidence-based feedback generated by AI based on student responses. Methodology: These materials were produced during professional development workshops (2025). All personal data has been anonymized.