ELEVATE：面向可扩展与包容性教育的人本生成式AI虚拟导师设计
ELEVATE: Designing Human-Centered GenAI Virtual Tutors for Scalable and Inclusive Education
AI总结：
提出ELEVATE框架，通过本地执行、多模态3D虚拟形象与教师治理层，实现隐私保护、低成本的生成式AI虚拟导师，解决云端文本聊天机器人存在的隐私、成本和数字鸿沟问题。
摘要：
生成式人工智能（GenAI），特别是大型语言模型（LLMs）的出现，正在重塑教育实践，同时也加剧了关于其应用的伦理争论。迄今为止，主导范式仍然是基于云端的纯文本聊天机器人：一种集中式服务，提供有限的教学控制、对知识源的弱透明度，以及隐私和法规遵从方面的重大风险。这种模式还假设持续连接和重复的API成本，为许多机构造成了结构性障碍，加剧了现有的数字鸿沟。同时，与LLM的教育互动可以从多模态线索和具身存在中受益，需要超越纯文本辅导的界面。在这项工作中，我们提出了ELEVATE（高效LLM教育与虚拟形象教学引擎），一个用于开发由知识基础设施治理的高效GenAI驱动虚拟形象导师的框架。ELEVATE将LLM驱动的对话与具身3D虚拟形象集成，实现多模态交互，并采用本地优先的执行模型，能够在消费级硬件上部署。该框架形式化了三层设计，将（i）面向学生的虚拟形象交互层、（ii）本地GenAI执行和多模态合成核心，以及（iii）面向教师的治理层分开。我们在真实世界的教育课程中实施并评估了一个工作原型。该系统在标准PC和智能手机上运行，我们提供了系统级性能证据，表明在现实硬件约束下具有响应式交互。最后，我们讨论了负责任采用的社会技术和教学意义，将ELEVATE定位为跨异构学校环境的隐私保护和包容性GenAI辅导的可扩展途径。
英文摘要：
The advent of Generative Artificial Intelligence (GenAI), and in particular Large Language Models (LLMs), is reshaping educational practice, while intensifying ethical debate about its adoption. To date, the dominant paradigm remains cloud-based and text-only chatbot: a centralized service that offers limited pedagogical control, weak transparency over knowledge sources, and non-trivial risks for privacy and regulatory compliance. This model also presumes continuous connectivity and recurring API costs, creating structural barriers for many institutions, reinforcing existing digital divides. At the same time, educational interaction with LLM can benefit from multimodal cues and embodied presence, requiring interfaces that move beyond text-only tutoring. In this work, we propose ELEVATE (Efficient LLM Education with Virtual Avatar Teaching Engine), a framework to develop efficient GenAI-driven avatar tutors governed by epistemic infrastructures. ELEVATE integrates LLM-driven dialogue with embodied 3D avatars for multimodal interaction and adopts a local-first execution model enabling deployment on consumer-grade hardware. The framework formalizes a three-stratum design that separates (i) a student-facing virtual avatar interaction layer, (ii) a local GenAI execution and multimodal synthesis core, and (iii) a teacher-facing governance layer. We implemented and evaluated a working prototype deployed in a real-world educational curriculum. The system runs on standard PCs and smartphones, and we provide system-level performance evidence to show responsive interaction under realistic hardware constraints. Finally, we discuss sociotechnical and pedagogical implications for responsible adoption, positioning ELEVATE as a scalable pathway for privacy-preserving and inclusive GenAI tutoring across heterogeneous school environments.
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