Artificial Intelligence (AI) is revolutionizing education by providing personalized learning experiences, but existing systems often lack a structured, autonomous framework for adaptive learning. This paper presents an Agentic Approach to AI-Assisted Education using the AI Value Protocol (AIVP) to enhance accountability, transparency, and incentive alignment in AI-driven education.

The proposed system consists of three specialized Small Language Model (SLM) agents working in concert:
- Tutor Agent – Responsible for explaining educational material dynamically, adapting its responses to the student's proficiency level and preferred learning style.
- Evaluation Agent – Focused on assessing student understanding through interactive assessments, quizzes, and contextual analysis of responses.
- Planning Agent – Conducts gap analysis by identifying weak areas in the student's knowledge and formulates a personalized education plan to be executed by the Tutor Agent.

By leveraging AIVP, the system ensures transparent verification of AI-generated educational content, incentivizing high-quality instruction and accurate assessments. The integration of a Proof of Stake Verifier (PoSV) mechanism further validates the credibility of AI-driven assessments, reducing bias and misinformation. This approach fosters a decentralized, AI-powered education system that is adaptive, scalable, and aligned with learner-specific needs.

Keywords: AI, Agentic, Education.