FairTutor让预算有限的学生也能获得接近高端AI辅导的公平教育体验。
FairTutor: Equity-Aware Pedagogical LLM Routing for Budget-Constrained AI Tutoring
- 通过多智能体协作动态分配模型资源，实现低成本高质量教学。
- 在多项任务中达到高端辅导97.1%的评分水平，成本降低71.6%。
- 适合关注教育公平与资源优化的AI教育研究者与实践者。
生成式AI导师提供实时个性化学习支持，但带来新的教育不平等：享有高级服务的学生能获得更清晰解释、更个性化指导和更好支架支持，而受限于免费或低费用服务的学生则处于劣势。为应对这一挑战，我们提出FairTutor——一种面向公平的模型路由框架，通过教学动机驱动的多智能体协同，实现成本可控的AI辅导。FairTutor结合查询分析、教学规划、低成本模型生成、评估引导的批判与修订，以及选择性升级至高端模型。我们引入访问层级教育（AIED）优势差距来衡量高端与预算受限辅导间的质量差异，并构建了涵盖数学、阅读、写作、科学和语言学习的TutorAccessEval基准。实证评估显示，FairTutor在地板调整后的李克特量表上达到高端教学97.1%的水平，同时将服务成本降低71.6%。敏感性分析揭示可调的代价-质量帕累托前沿，使FairTutor可根据不同学生群体需求灵活配置。
原文摘要 · Abstract (English)
Generative AI tutors provide real-time, personalized learning support, but also create a new education inequity: students with access to premium AI services may receive clearer explanations, more personalized guidance, and better scaffolding than students limited to free or low-cost services. To address this challenge, we propose FairTutor, an equity-aware model-routing framework that achieves cost-effective AI tutoring via pedagogically motivated multi-agent orchestration. FairTutor combines query analysis, pedagogical planning, low-cost model generation, evaluator-guided critique and revision, and selective escalation to premium AI models. We introduce access-tier AI Education (AIED) Advantage Gap to measure the quality difference between premium-access and budget-constrained tutoring, and TutorAccessEval, a benchmark spanning math, reading, writing, science, and language learning. Empirical evaluations show that FairTutor achieves 97.1% of premium pedagogical quality (in floor-adjusted Likert scale) while reducing serving cost by 71.6%. Sensitivity analysis reveals a tunable cost--quality Pareto frontier, enabling FairTutor to be tailored to the needs of diverse student populations.
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