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AGI-Powered Adaptive Tutor & On-Chain Credentialing Framework

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AGI-Powered Adaptive Tutor & On-Chain Credentialing Framework

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nblogist May 25, 2025
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Challenge: Open challenge

Industries

Algorithmic/technicalCommunity and CollaborationLearning and education

Technologies

AGI R&DBlockchain & infrastructureMeTTa

Tags

CryptoDF rules

Description

Develop a modular AGI-powered tutor that balances teaching, quizzing, and review drives using Hyperon/PRIMUS’s motivation engine, dynamically adapting content to each learner’s knowledge state and confidence, then issues tamper-proof on-chain credentials for every completed milestone—pioneering adaptive AI education with verifiable badges.

Detailed Idea

Alignment with DF goals (BGI, Platform growth, community)

This project showcases a high-impact, human-centered AGI application—adaptive tutoring—that directly leverages Hyperon’s ECAN for dynamic attention allocation and MeTTa for motivational control. By open-sourcing the demo and publishing on Vercel/GitHub, we drive platform growth and community engagement. On-chain credentialing strengthens SingularityNET’s ecosystem by proving real-world utility of decentralized AI services.

Alignment with DeepFunding goals:
This proposal drives the SingularityNET/DeepFunding ecosystem forward by:
1.Platform growth: A publicly deployed demo + open-source GitHub repo invites community feedback, contributions, and proof-of-concept use.
2.BGI & R&D leadership: It showcases a real-world AGI application (adaptive tutoring) that leverages Hyperon’s ECAN, DAS, and MeTTa components, inspiring further research and grant activity.
3. Community engagement: Learners, educators, and developers can participate in study-group calls, file issues/pull requests, and extend the framework, strengthening the DF developer network.

Problem description

Most online learning platforms deliver static content, failing to adapt in real-time to a learner’s confidence or performance, which leads to low engagement and poor outcomes. Meanwhile, digital certificates are easily forged, undermining employer trust in online credentials.

Proposed Solutions

Embed a MeTTa/PRIMUS motivation engine to balance “teach,” “quiz,” and “review” drives based on learner feedback. Prototype a Node.js/React demo where quiz scores and self-ratings dynamically reshape lessons. Mint each completed module as an ERC-721 badge on a public testnet, creating tamper-proof, verifiable educational credentials.

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