为教育AI设计可验证、可审计的可信标准，让开源模型也能安全使用。
TEAS: Trusted Educational AI Standard: A Framework for Verifiable, Stable, Auditable, and Pedagogically Sound Learning Systems
- 构建四支柱框架：可验证性、稳定性、可审计性、教学合理性。
- 证明系统化架构比模型大小更能决定AI可信度。
- 适合教育机构部署AI时评估可靠性，尤其关注公平与安全。
AI快速融入教育领域，但重能力轻可信度带来风险。实际应用显示，即使先进模型也需复杂架构保障可靠。现有评估体系碎片化：政策缺技术验证，教学指南假定模型可靠，技术指标脱离教育场景。本文提出TEAS（可信教育AI标准）框架，包含四大支柱：(1) 可验证性，内容基于权威来源；(2) 稳定性，确保核心知识确定性；(3) 可审计性，支持机构独立验证；(4) 教学合理性，遵循主动学习原则。我们主张可信度源于系统架构而非模型规模，表明低成本开源模型也可达部署级可信，为全球安全引入AI教育提供可扩展、公平的路径。
原文摘要 · Abstract (English)
The rapid integration of AI into education has prioritized capability over trustworthiness, creating significant risks. Real-world deployments reveal that even advanced models are insufficient without extensive architectural scaffolding to ensure reliability. Current evaluation frameworks are fragmented: institutional policies lack technical verification, pedagogical guidelines assume AI reliability, and technical metrics are context-agnostic. This leaves institutions without a unified standard for deployment readiness. This paper introduces TEAS (Trusted Educational AI Standard), an integrated framework built on four interdependent pillars: (1) Verifiability, grounding content in authoritative sources; (2) Stability, ensuring deterministic core knowledge; (3) Auditability, enabling independent institutional validation; and (4) Pedagogical Soundness, enforcing principles of active learning. We argue that trustworthiness stems primarily from systematic architecture, not raw model capability. This insight implies that affordable, open-source models can achieve deployment-grade trust, offering a scalable and equitable path to integrating AI safely into learning environments globally.
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