EduAccess AI

EduAccess AI: Decentralized Global Education Intelligence Subnet

EduAccess AI is a Bittensor subnet that rewards miners with incentives for generating and spreading the very localized and multilingual educational content and assessments for the more than 260M learners, who are regarded as underserved and worldwide. The validators evaluate on three dimensions: cultural relevance, fact accuracy, and learning outcomes, thereby producing continuous "proof of learning intelligence" that is streamable. The emissions of TAO are connected directly to the measurable success of quizzes and the retention rates of real-world users.

Incentive & Mechanism Design

Emission and Reward Logic: 70% TAO to miners based on composite score (accuracy 40%, cultural relevance 30%, speed 20%, novelty 10%); 20% to validators; 10% subnet treasury for regional bounties.

Incentive Alignment: Validators stake TAO to challenge miner scores—slashed for collusion. Miners are penalized through sybil-resistant compute proofs and cross-miner plagiarism detection.

Anti-Adversarial Measures: Dynamic difficulty scaling; geo-fenced prompts prevent spamming; and mandatory compute traces verify genuine intelligence vs. rote copying.

Proof of Intelligence: Tangible learning outcomes (90%+ quiz comprehension) rather than raw compute are measured and validated through community staker feedback.

Miner Design

Miner Tasks: Handle geo-tagged prompts such as "Create a 5 min Swahili lesson on drought-resistant crops for rural Kenya farmers and a 5-question quiz."

Input → Output Format:

text

{

"content": {"text": "...", "audio_url": "...", "image_urls": []},

"quiz": [{"question": "...", "options": [...], "correct": 2}],

"metadata": {"language": "swahili", "region": "kenya_rift_valley", "skill_level": "beginner"}

}

Performance Dimensions: 95% factual accuracy, <5s latency, 90% simulated user comprehension, cultural appropriateness score >8.5/10.

Validator Design

Scoring Methodology: LLM rubric (Scoring Methodology: LLM rubric (accuracy, relevance) + staker upvotes weighted by delegation + cross-miner response consensus.

Evaluation Cadence: Continuous 10-minute query rounds; daily leaderboards; epoch-end final rankings.

Validator Incentive Alignment: Earn TAO proportional to scoring accuracy vs. ground truth datasets; delegation bonuses from NGOs/schools amplify high-integrity validators.

Business Logic & Market Rationale

Problem: 260M children lack context-relevant education; centralized edtech ignores 7,000+ dialects and rural realities.

Competing Solutions:

• Centralized: Khan Academy/Duolingo (urban languages only)

• Bittensor: No existing education subnets; closest are text generation (SN1) or general AI (SN13)

• External: Google Translate + Wikipedia (no curriculum structure)

Why Bittensor: Perfect for crowdsourced localization; miners specialize by region/language; validators ensure quality control; TAO aligns long-term incentives.

Adoption Path:

1. Testnet: 100+ miners, 10 regions, live metrics

2. Phase 2: NGO partnerships (UNICEF, Pratham)

3. Phase 3: Government integrations (DIKSHA India, Brazil MEC)

4. Revenue: Premium APIs for schools, staker ROI from adoption

   

Sustainable Business: 20% literacy gains → economic mobility → staker demand → subnet value appreciation.