TEAS: Trusted Educational AI Standard: A Framework for Verifiable, Stable, Auditable, and Pedagogically Sound Learning Systems
It addresses the risk of unreliable AI in educational systems for institutions globally, offering a scalable and equitable solution.
The paper tackles the problem of untrustworthy AI in education by introducing TEAS, an integrated framework with four pillars (verifiability, stability, auditability, and pedagogical soundness) to ensure deployment readiness, arguing that affordable models can achieve trust through systematic architecture.
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.