Definition #
Machine learning models that dynamically adjust course difficulty, content types, and pacing based on real-time student interactions.
Key Characteristics #
- Competency mapping
- Micro-adaptive adjustments
- Engagement prediction
- Multi-modal delivery
Why It Matters #
Increases knowledge retention by 45% compared to static curricula (McGraw-Hill 2023 Report).
Common Use Cases #
- Corporate compliance training
- K-12 math remediation
- Language learning apps
Examples #
- Khan Academy’s practice system
- Duolingo’s difficulty scaling
- Smart Sparrow adaptive platforms
FAQs #
Q: Data privacy concerns?
A: FERPA-compliant systems anonymize student IDs and encrypt progress data.
Q: Human teacher role?
A: AI handles pacing/content, teachers focus on mentorship and complex Q&A.