The problem MentorMesh solves

Traditional mentorship platforms are centralized, inefficient, and lack transparency. Students struggle to find mentors aligned with their skill levels, while mentors face challenges in managing payments, schedules, and trust.

How It Helps:

• For Students:

  • Find the right mentor effortlessly using AI-powered skill assessments.
  • Transparent and secure payment via EduChain's decentralized infrastructure.
  • Personalized learning paths tailored to their proficiency level (Beginner, Intermediate, Expert).

• For Mentors:

  • Set flexible schedules, rates, and teaching preferences without intermediaries.
  • Receive payments directly through Web3-enabled smart contracts, ensuring fairness.
  • Build credibility in a transparent, blockchain-backed ecosystem.

Why It’s Better:

• Decentralization: Eliminates middlemen, reducing costs and increasing trust.
• AI Matching: Ensures accurate pairing of students and mentors, saving time and effort.
• Secure Transactions: Blockchain ensures tamper-proof records and fraud prevention.

This platform makes mentorship accessible, efficient, and trustworthy, empowering learners and educators worldwide.

Challenges we ran into

Building this decentralized mentorship platform presented several technical and conceptual hurdles. Below are the key challenges we faced and how we overcame them:

1. Integrating AI with Blockchain (EduChain):
   Bridging the AI quiz engine with EduChain’s decentralized infrastructure was complex. The AI needed real-time blockchain data to assess students and match them with mentors. To solve this, we used APIs and developed lightweight middleware, ensuring smooth communication without compromising speed or security.
2. Smart Contract Bugs During Testing:
   Early versions of our smart contracts had issues, such as failing to release payments to mentors after sessions. This created trust concerns for users. Using tools like Remix IDE and Hardhat, we debugged and optimized the logic, ensuring secure and reliable transactions on the testnet.
3. Onboarding Users to Web3:
   Many users were unfamiliar with connecting wallets or managing private keys, causing friction during onboarding. We simplified the process by integrating MetaMask and providing step-by-step guidance.
4. Scalability of AI Quizzes:
   Generating dynamic quizzes for multiple users in real time strained server resources, slowing response times. To address this, we optimized the AI model by hosting it on a scalable cloud platform and implemented caching mechanisms for frequently accessed questions, significantly improving performance.
5. Ensuring Data Privacy:
   Storing sensitive information like meeting links and personal details on-chain raised privacy concerns. We adopted a hybrid approach—storing only transaction hashes and metadata on-chain while encrypting sensitive data off-chain using IPFS and Lit Protocol.
6. Cross-Platform Compatibility:
   Ensuring seamless functionality across devices required extensive adjustments. We used React.js for responsive design.