Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System

Jul 16, 2026

This preprint reports the first classroom deployment of LEA, an adaptive AI tutoring agent, with real students and evaluates its scalability across three different courses. The study finds that synthetic (simulated) evaluation does not fully predict real-world classroom performance: while answer relevancy and context precision remain stable across courses, faithfulness of responses declines as the curriculum diverges from the system's original subject. These results highlight the need for further research into making AI tutoring systems fully course-agnostic.

Why it matters: This work provides early empirical evidence on the challenges of deploying AI tutoring systems in real classrooms and exposes the limitations of relying solely on synthetic evaluation for predicting real-world performance.

Full story at: arXiv Computers and Society ↗