AAiCC: M7S2 – AI for Personalized Learning Paths

Описание: Design equitable, AI-supported personalized learning paths that offer multiple routes to mastery. In this session, we explore how generative AI can help educators create adaptive content at different readiness levels while maintaining consistent rigor and preventing hidden tracking. Learn how to balance efficiency with intentional design so personalization increases access without lowering expectations.

We examine how AI can generate foundational, standard, and advanced content versions in minutes, and how to embed equity safeguards from the start. You will build a personalized learning framework for one of your course topics, then test it for unintended bias, unequal rigor, or pathway barriers. The goal is not just differentiation, but genuine flexibility and student agency.

🧠 LEARN Focus
🎧 Listen: Understand how AI accelerates adaptive content design and equity checks
🧭 Explore: Build a personalized learning framework with multiple readiness pathways
✍️ Apply: Create an equitable, flexible learning package for a real course topic
🤔 Reflect: Examine risks of hidden tracking and unequal rigor
🤝 Network: Share your learning framework and provide peer feedback on equity and flexibility

📚 Learn more and connect
🎓 Enroll in the Canvas course: https://cptc.instructure.com/courses/...

📺 Watch the full series:    • Applied AI in College Classrooms  

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🌐 Connect with the course creator:   / rlethcoe