SOURCE-LINKED INTELLIGENCE
Simulating Disengaged Students to Evaluate LLM-based Tutors
Simulated students generated by computational models provide a practical way to evaluate tutoring strategies and pedagogical approaches used by human and AI tutors. However, such simulations should account for disengaged behaviors, including gaming the system, wheel-spinning, and off-task behavior, because tutors may need different responses for different learner states. We present Disengagement-Aware Student Simulators (DAS2), a reproducible pre-deployment protocol that models five learner-engagement states: engaged, gaming, wheel-spinning, off-task, and mixed, and evaluates AI tutor performa
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T01:28:48.000Z
First collected: 2026-09-20T18:42:18.733Z. This is not the publication date.