Live Link: https://www.eduverse.network/
Problem Statement
Standardized education overlooks individual learning styles and paces, leading to disengagement and hindering student potential. Students and educators face the challenge of a rigid system that doesn’t adapt to diverse needs, impacting learning outcomes.
Solution Overview
Our AI-Powered Personalized Education Platform offers a dynamic solution by employing a multi-agent system built on the Alith Agentic Framework. Specialized AI agents collaboratively analyze individual learning styles, curate tailored content, provide adaptive tutoring, and track progress in real time. This creates a uniquely personalized learning journey that adjusts to each student’s needs and pace. By integrating diverse educational resources, the platform aims to enhance engagement, improve learning outcomes, and empower both students and educators.
Project Description
This platform is designed to benefit a wide range of users across the educational landscape. Students of all ages, from kindergarten through higher education, can experience a more engaging and effective learning journey tailored to their specific needs. Educators can utilize the platform to enhance their teaching capabilities, gain deeper insights into student progress, and free up time for more individualized support.
On-Chain Components: A First-Class Implementation and Core Requirement for Blockchain Deployment
Our AI-powered personalized education platform is not merely “blockchain-enhanced” — its core functionality and unique value proposition depend fundamentally on blockchain deployment via Hyperion. The decentralized, trustless, and immutable nature of blockchain—particularly Hyperion’s AI-optimized environment—is essential to realizing our vision.
On-Chain Achievement Verification (Smart Contract Architecture)
What’s On-Chain:
The LearningRecord.sol smart contract serves as an immutable, on-chain ledger for all student accomplishments.
- Achievement Mapping: The contract maintains a mapping from a user’s wallet address to an array of
Achievementstructs, where each struct contains themoduleNameand atimestamp. - Secure, Off-Chain Verification: A secure, server-controlled wallet is the only address authorized to call the
addAchievementWithSignaturefunction. This ensures achievements are only recorded after the platform’s backend has verified the user’s quiz completion and signature.
Why It Needs Blockchain:
- Trustless & Immutable Credentials: By recording achievements on the Metis Hyperion testnet, we create a permanent, tamper-proof record of learning that is owned by the user and verifiable by anyone (e.g., employers, other institutions) without relying on a centralized database.
- Data Sovereignty: Students retain full control of their learning data. On-chain metadata ensures ownership and transparency, unlike siloed centralized platforms.
- Foundation for a Trustless Ecosystem: This on-chain record is the foundational layer. While AI agents currently operate off-chain for performance, their most critical output—the certification of learning—is secured on the blockchain.
AI Enablement via the Alith Agentic Framework
What is Alith:
Alith is a modular, multi-agent framework designed to power personalized learning by simulating human-like reasoning, memory, and collaboration among AI agents. It forms the intelligence layer of our platform, with deep interoperability with blockchain systems like Hyperion.
Key Features of Alith (Implemented):
- Agent Management: Modular deployment of specialized AI agents:
- Learning Style Analyst (Implicit in course selection)

- Content Curator (AI Study Guide Generator)

- Personalized Tutor (Quiz Hints & Tutor Chat)

- Progress Tracker (On-chain achievement logging)

- Learning Style Analyst (Implicit in course selection)
- Persistent Memory: Agents maintain long-term memory and context across sessions (demonstrated in Telegram bot), enhancing personalization.
- Toolchain Access: Seamless access to external APIs and knowledge bases for generating content and hints.
Blockchain Integration with Alith:
The Alith agent framework operates off-chain to provide a responsive and intelligent user experience. The blockchain is used as the ultimate source of truth for the results of these AI interactions.
- Verifiable Outcomes: When a user successfully passes a quiz, the off-chain backend coordinates with the
ProgressTrackerAgentto commit this achievement to theLearningRecordsmart contract, creating an immutable recordâś…. - Foundation for On-Chain Logic: The current architecture provides the groundwork for future enhancements where agent workflows could be triggered and validated by smart contracts.
Secure, Real-Time AI Inference (Leveraging Hyperion’s AI-Native Infrastructure)
What’s On-Chain (or Hyperion-enabled):
While heavy AI workloads (e.g., model training, long-form inference) run off-chain via the Alith framework, Hyperion enables:
- Verifiable AI Outputs: AI-driven assessments and tutor recommendations are validated off-chain, with the final “proof-of-completion” immutably stored on-chain. This ensures educational integrity and transparency—unlike opaque, centralized AI systems.
- Low Latency, High Throughput: Hyperion’s parallel execution environment supports the fast transaction finality needed to record achievements in near real-time, keeping learners engaged.
Data Sovereignty, Incentives, and Trust in Our Education Platform
Our AI-Powered Personalized Education Platform leverages Hyperion’s blockchain to give students true control over their learning data and foster a trusted educational environment.
Wallet-Based Identity and User Control
In our platform, a user’s Web3 wallet (e.g., MetaMask) serves as their identity. Authentication is handled securely via message signing, ensuring users control access to their accounts without traditional passwords. This wallet-centric approach is the first step toward a future of true data sovereignty, where learning profiles can be fully controlled by the user.
Importance of Trust and Transparency
In education, trust and transparency are paramount:
- Learner Trust: Students need to trust that their achievements are real. On-chain verifiable credentials build this crucial trust.
- Empowering Educators: Transparent data provides educators with reliable insights into student progress and curriculum effectiveness.
- Equity & Accessibility: Blockchain prevents fraud and offers a universal, trusted way for students to showcase skills, democratizing opportunities regardless of background.
Community Engagement Features
Peer Study Groups: Connect with others for collaborative discussions and support.
Q&A Forums: Ask and answer subject-related questions to deepen understanding.
Simulated Collaboration: Practice teamwork in virtual, project-based learning scenarios.
Optional Mentorship: Receive guidance from experienced peers and educators.
Progress Sharing (Privacy-Controlled): Share milestones to motivate peers while maintaining control over visibility.
Community Challenges: Participate in gamified group activities to promote engagement and friendly competition.
Future Roadmap & Planned Enhancements
This MVP is the foundation for a much larger vision. Our planned enhancements include:
-
Token-Based Incentives ($LP)
- Introduce a utility token, LearnPoints ($LP), to create a Learn-to-Earn (L2E) model that rewards students for completing modules, mastering skills, and contributing to the community.
- $LP could be staked for premium features or used to vote on platform decisions, aligning community interests.
-
Decentralized Agent Marketplace
- Allow community developers to contribute new agents (e.g., specialized tutors or regional content curators).
- Agents will be published and authenticated on-chain with verifiable capabilities.
- Token-based reputation and staking mechanisms will prevent misuse or malicious behavior.
-
On-Chain Agent Coordination & Composable Workflows
- Evolve our smart contract architecture to manage agent workflows directly, enabling fully trustless and transparent educational logic.
- Enable educators to compose reusable learning workflows by chaining agents together via simple declarative schemas, backed by on-chain validation.
-
ZK-Verifiable Inference
- Integrate zkML (zero-knowledge machine learning) to enable cryptographic verification of off-chain AI outputs. This allows validators and institutions to confirm an agent’s decision (e.g., a test score) without exposing the underlying model or input.
-
Multilingual & Accessibility Agents
- Create plug-and-play AI agents specialized in localization, translation, and neurodiverse learning strategies.
Team Members
GitHub
https://github.com/amardeepio/Eduverse
Video tutorial EduVerse tutorial video
Presentation and future roadmap: Presentation and RoadMap

