FairTutor: 预算受限AI辅导中的公平感知教学LLM路由
FairTutor: Equity-Aware Pedagogical LLM Routing for Budget-Constrained AI Tutoring
AI总结：
提出FairTutor框架，通过教学驱动的多智能体编排实现成本效益AI辅导，在降低71.6%服务成本的同时达到97.1%的优质教学质量，并引入AIED优势差距指标衡量公平性。
摘要：
生成式AI辅导提供实时、个性化的学习支持，但也造成了新的教育不平等：能够使用高级AI服务的学生可能比仅限于免费或低成本服务的学生获得更清晰的解释、更个性化的指导和更好的支架。为了解决这一挑战，我们提出了FairTutor，一个公平感知的模型路由框架，通过教学驱动的多智能体编排实现成本效益的AI辅导。FairTutor结合了查询分析、教学规划、低成本模型生成、评估者引导的批评与修订，以及选择性升级到高级AI模型。我们引入了访问层级AI教育（AIED）优势差距来衡量高级访问与预算受限辅导之间的质量差异，以及TutorAccessEval基准测试，涵盖数学、阅读、写作、科学和语言学习。实证评估表明，FairTutor在降低71.6%服务成本的同时，达到了97.1%的高级教学质量（基于地板调整的李克特量表）。敏感性分析揭示了一个可调的成本-质量帕累托前沿，使FairTutor能够针对不同学生群体的需求进行定制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
