This paper introduces FairTutor, a framework for cost-effective and equitable AI tutoring that intelligently routes student requests to different AI models.

In plain terms

Generative AI tutors offer personalized learning support, but a problem arises when premium (expensive) services provide better help than free or low-cost ones, creating an "education inequity." To address this, researchers developed FairTutor, a system that manages how AI tutors respond to students. FairTutor first analyzes a student's question and plans a teaching approach, then attempts to generate an answer using a cheaper AI model. An "evaluator" AI checks the quality of this initial answer, guiding the cheaper model to revise it if needed. Only when necessary for complex problems or insufficient answers does FairTutor selectively use a more expensive, premium AI model, a process called "multi-agent orchestration." This approach achieved 97.1% of the teaching quality of using only premium AI, while drastically cutting serving costs by 71.6%.