AI Task Agent + MCP

4.1 AI Task Agent + Multi-Context Protocol (MCP)

The AI Task Agent is the intelligent core for task generation and assessment, dynamically creating personalized learning tasks based on user behavior, knowledge graphs, and social interactions. The Multi-Context Protocol (MCP), an open standard introduced by Anthropic in 2024, serves as the underlying communication framework, enabling seamless integration between the AI model and diverse data sources.

• Dynamic Task Generation via Knowledge GraphsThe AI Task Agent employs knowledge graphs to model users’ current knowledge, interests, and historical performance. By referencing educational frameworks like Bloom’s Taxonomy, it generates tasks with precisely calibrated difficulty levels, ensuring personalized challenges and immediate feedback. Advanced natural language processing (NLP) and graph neural networks (GNNs) enable semantic understanding of user inputs, enhancing task relevance.

• Operational Workflow

  1. Initialization: The system loads a user’s knowledge graph, historical performance, and social behavior data upon task initiation.

  2. Context Aggregation: The AI Task Agent queries MCP interfaces to fetch contextual data from local/remote sources, including past incorrect answers, trending topics, and peer challenges.

  3. Task Synthesis: Using a transformer-based generative model fine-tuned for educational content, the Agent constructs highly relevant tasks and learning paths.

  4. Feedback Loop: Post-task results are analyzed, updating the user’s knowledge graph and triggering personalized recommendations for subsequent tasks.

• MCP: Bridging Data and IntelligenceMCP is a standardized protocol enabling large language models (LLMs) to securely and efficiently interact with external data sources, tools, and on-chain resources. In Quizon, MCP facilitates real-time access to:

  • On-Chain Data: User achievements, learning milestones, and token balances stored on the blockchain.

  • Local Data: Device-stored learning progress and preferences, ensuring privacy-preserving personalization.

  • Social Context: Community trends, peer interactions, and leaderboard dynamics.MCP employs zero-knowledge proofs (ZKPs) to validate data integrity without compromising privacy, making it a critical infrastructure for scalable, context-aware AI applications. Additionally, MCP supports modular integration with decentralized storage solutions (e.g., IPFS, Arweave) and oracles for real-time data feeds, enabling Quizon to adapt to evolving user and ecosystem needs.

• Technical Advantages

  • Scalability: MCP’s modular design supports integration with multiple blockchains (e.g., Ethereum, Polygon) and data protocols.

  • Interoperability: Compatibility with emerging AI standards ensures future-proofing against model advancements.

  • Privacy: Encrypted data pipelines and ZKPs safeguard user information during cross-system interactions.