Introduction - Voices from the Margins
Chapter 1 - Introduction to AI in Schools
Conflict Theory: How It Can Help Us Understand AI in Schools
Preparing for AI: Key AI Concepts for Educators to Know
Troubling Trends: Biases, Harms, and Conflicts With AI in Schools
Chapter 2 - Examining Bias in Educational AI
Casting a Critical Eye: How Bias Manifests in School Data
Problematic Cases: Scenarios on Biased AI in Schools
Tackling Bias: Fixing Unfair Bias in Educational AI
Chapter 3 - The Digital Divide in Schools
Connecting the Dots: Marginalization, Access, and Classroom AI
Promoting Equity: Uneven Access to AI for Creativity and Learning
Tools for Equity: Making Educational AI More Equitably Accessible
Chapter 4 - AI and High-Stakes Decisions in Schools
Careful Considerations: Admissions, Grading, Tracking
Propagating Partiality: AI's Potential to Perpetuate Injustice
Transparent and Accountable: Fostering Trust Through Disclosure
Chapter 5 - Synthetic Media in the Classroom
Creating Counterfeits: Fake Audio, Images, and Video
Protecting Students: Dangers of Deepfakes in Education
Teaching Awareness: Identifying and Mitigating Fake Media
Chapter 6 - Student Data, AI Power
Collecting and Controlling: Student Data to Train AI Systems
Privacy Concerns: Risks and Harms From using Student Data
Taking Charge: Student Data Rights and Responsibilities
Chapter 7 - Toward More Equitable Classroom AI
Challenging the Status Quo: Addressing AI Harms
Principles for Practice: Reducing Risks and Bias
Taking Action: The Role of Activism
Conclusion
Glossary
Endnotes
About The Authors
