AI for Teaching Course

Contents

1. How to use this book
2. Chapter 1. Instructional Design and Learning Materials

   Learning materials start from objectives · Five elements of a prompt · Revising prompts: From symptoms to causes · Factual errors in AI-generated learning materials · Things that are not verification · Verifying with primary sources · Bias and stereotypes · Differentiated materials: Changing expression while keeping objectives · Copyright: Using others' writings in class materials · AI-rewritten text and AI-generated images · Risk-based verification · The process of producing AI learning materials · Does AI with search capabilities solve the problem? · Differentiated materials and the trap of low expectations · Borderline cases among private academies, universities, and schools

3. Chapter 2. Grading, Feedback, and Student Assessment (High-Impact Area)

   High-impact artificial intelligence and student assessment · Who bears the obligations · Prior review and requests for confirmation · Responsibilities of high-impact AI business entities · Transparency: Prior notice and labeling · Error types in automated grading · The trap of averages · Test reliability first · Utilization levels according to assessment purpose · Adapting grading rubrics for AI use · Rubric-based feedback · Procedures for finalizing AI feedback · Responding to student questions and appeals · What "a human verified it" actually means · Appeals are quality signals · What changes in higher education and adult education

4. Chapter 3. Student Data and Child Protection

   When Student Writing Constitutes Personal Information · Principles of Processing · Legal Grounds for Processing and Schools · Children Under 14 Years of Age · Use Beyond the Original Purpose and Provision · School Records and Provision to Third Parties · Sensitive Information · External AI Services and Entrustment · Five Questions for Reading Data Terms · Age Criteria and Guardian Notices · Methods for Using AI Without Student Information · End of Processing: Destruction and Incident Response · Is writing with names removed anonymous? · When terms-of-service age and statutory age differ · Whether to open student accounts or have the teacher demonstrate

5. Chapter 4. Academic Integrity and AI Use Policies

   The questions of academic integrity have changed · From assignment objectives to permission levels · Writing permission level instructions · AI Usage Log · Rules for disclosure and citation · Why AI detectors cannot serve as evidence · Designing to verify authorship without detectors · Procedures when suspicion arises · With and without standards · Types of risks arising from student AI use · Creating school-wide standards · Beyond rules to literacy · How to read "98% accuracy": The base rate trap · The cost of a ban policy · Who is an oral check fair to?

6. Chapter 5. Institutional Adoption and Operations

   Adoption begins with problem definition · Performance indicators aligned with the purpose · UNESCO guidance for generative AI in education and research · UNESCO AI competency framework for teachers · Service selection and contracting · Phased adoption · Teacher training and support · Informing guardians and students · Translating the NIST AI RMF into schools · Inspection items and intervals · Tutors that manage knowledge bases · Incident types and initial responses · Pitfalls of measuring effectiveness · Why consumer apps are appealing and their cost · Governance is iteration, not a document

7. Appendix A. Glossary
8. Appendix B. Self-check answers and explanations
9. Appendix C. Exam day guide
10. Appendix D. Reference documents