当AI学会“仿真思维”，教师才能回归育人本质

TL;DR - Tianli Qiming and research partners introduced a cognitive-world-model approach to personalize student learning while reducing teachers’ routine workload. It aims to move education AI beyond basic content recommendation toward modeling learning gaps and protecting sensitive cognitive data.

• The system automates learning diagnosis, resource matching, exercise adaptation, and classroom analytics.
• Tianli Qiming reports deployments across 150+ schools serving 300,000+ teachers and students.
• Its LAM framework models students’ cognitive states to infer misconceptions and plan personalized learning paths.
• A proposed “cognitive sovereignty” framework applies tiered protection to inferred student data.