🚨 "Design and development of an AI-powered learning path generator, Vocational Pathway Navigator with Dynamic Career Intelligence and NSQF-Integrated Learning Ecosystem"
- Theme: 2. AI in Education & Skilling
- Category: Software
- Smart Pathway Engine – AI analyzes learner profiles to generate personalized NSQF-aligned career routes
- AI Matching Engine – Smart course/curriculum matching based on user's input
- Career Journey Gamification – Achievement unlocks, skill mastery levels, industry challenges & leaderboards
- AI Learning Companion – Real-time guidance, industry alerts, skill forecasts & content recommendations
- Personalized NSQF Pathways – AI matches 50+ learner parameters to 139+ government courses with 95% accuracy
- Real-Time Market Alignment – Dynamic integration with labor market intelligence ensures pathway recommendations adapt to industry demands, emerging skills, and regional employment opportunities
- Multilingual Accessibility – 12+ Indian languages with voice navigation for diverse demographics
- Predictive Career Intelligence – AI forecasts employment probability & salary potential with 3-5 year projections
- Adaptive Pathway Evolution – Routes auto-adjust based on progress, industry changes & skill demands
- Gamified Engagement – Duels and streaks
- Cross-Sector Mobility – AI identifies transferable skills enabling seamless career transitions
- High Demand – Diverse learner backgrounds demand tailored skilling pathways
- Industry Alignment – Labour market intelligence ensures relevance to evolving job roles
- Future-Proofing – Adaptive AI pathways enable lifelong learning and stackable skills
- Institutional Backing – NCVET & MSDE integration provides credibility and adoption push
- User Trust: Learners may hesitate to rely on AI-driven career guidance
- Data Accuracy: Incomplete or outdated learner and labour market data may reduce recommendation quality
- Bias & Fairness: Risk of unequal opportunities if algorithms favor certain demographics or regions
- Long-Term Adoption: Sustaining engagement as career needs evolve requires continuous system updates
- Trust Building: Explainable AI, counselor support, and transparent recommendation logic
- Data Quality: Regular updates from NSQF, labour market intelligence, and verified providers
- Fairness & Equity: Bias audits, inclusive design, and multilingual accessibility
- Sustained Engagement: Adaptive pathways, career milestone tracking, and continuous upskilling prompts
- 75% of Indian learners gain career benefits from AI-driven personalized paths
- 90% of employers prioritize NSQF-aligned micro-credentials in hiring
- India's EdTech market expected to surpass $10B by 2025, led by mobile-first apps
- AI-led adaptive learning speeds up skill acquisition by 30-40% versus traditional means
- 1+ billion Indian workers need reskilling by 2030 due to tech change and automation
- AI Personalization: AI creates customized learning paths that boost engagement and results
[Frontiers in Education 2024 | Emerald AI in Education] - Vocational Skilling & NSQF: NSQF links vocational training with industry and job market demands
[ICRIER NSQF Note | IJFMR NSQF Implementation] - Labour Market Intelligence: Real-time labour data integration is vital for future-ready skills
[India Employer Forum 2025 | Economic Times EdTech] - India EdTech Growth: EdTech is a $10B+ market growing quickly with AI-powered, mobile-first solutions
[Market Research Future | HolonIQ Charts 2025]
💡 Takeaway: Research validates the importance of AI-personalized learning, NSQF compliance and scalable secure design for India's skill ecosystem.
- Pathway Recommendation Accuracy (Target: 95%)
- User Engagement Rate with AI Learning Companion
- NSQF Course Completion Rates
- Employment Outcome Tracking (6-month post-completion)
- Multi-language Adoption Metrics
- Labor Market Alignment Score
- User Satisfaction & Trust Scores
- Students & Youth: Personalized career pathways aligned with market demands
- Job Seekers: Data-driven career transitions with employment probability forecasts
- Working Professionals: Continuous upskilling with adaptive learning paths
- Educational Institutions: NSQF-integrated curriculum planning support
- Government Schemes: Enhanced effectiveness of Skill India missions through AI optimization
| Platform | Supported? |
|---|---|
| Web (any browser with JS functionality) + Fully Responsive | ✅ |
| Android (non-natively through WebView) | ✅ |
- Node.js 18+
- Python 3.11+
- Docker & Docker Compose
- PostgreSQL 15 (for local development)
cd frontend-web
npm install
cp .env.template .env.local
npm run devSee DEPLOYMENT_FULL.md for complete deployment instructions.
# Core API
cd backend_1-core_service
docker-compose up -d
# AI Engine
cd ../backend_2-ai_engine_service
docker-compose up -d
# AI Companion
cd ..//backend_3-ai_companion_service
docker-compose up -dgraph TB
subgraph Clients
Web[Web App<br/>Next.js]
end
subgraph "ShikshaDisha Backend Services"
subgraph "backend-core :8000"
API[Core API<br/>FastAPI]
DB[(PostgreSQL)]
Redis[(Redis)]
Celery[Celery Workers]
WS[WebSocket<br/>Real-time]
end
subgraph "backend-2-ai_engine_service :9000"
Matcher[AI Matching<br/>Engine]
FAISS[FAISS Index]
Embed[Sentence<br/>Transformers]
Behavior[Behavior<br/>Analyzer]
end
subgraph "/backend_3-ai_companion_service :9001"
Chat[AI Companion<br/>Chat]
Forecast[Skill<br/>Forecaster]
Alerts[Industry<br/>Alerts]
Rec[Content<br/>Recommender]
end
end
Web --> API
Web --> Matcher
Web --> Chat
API --> DB
API --> Redis
API --> Celery
API --> WS
Matcher --> FAISS
Matcher --> Embed
Matcher --> Behavior
Chat --> Forecast
Chat --> Alerts
Chat --> Rec
| Service | Port | Technology | Purpose |
|---|---|---|---|
| backend-core | 8000 | FastAPI + PostgreSQL | User management, actions, notifications, sessions, streaks |
| backend-2-ai_engine_service | 9000 | FastAPI + FAISS | Course matching, behavior analysis, recommendations |
| /backend_3-ai_companion_service | 9001 | FastAPI + Redis | AI chat, skill forecasting, alerts |
- User Actions → Core API → PostgreSQL + Celery Workers
- Course Matching → AI Engine → FAISS Vector Search → Semantic Similarity
- AI Companion → Skill Forecasts + Content Recommendations
- Frontend: React, Next.js 14, TypeScript, TailwindCSS, shadcn/ui
- Backend: Python FastAPI (3 microservices)
- Database: PostgreSQL 15
- Cache/Queue: Redis 7
- AI/ML: Sentence Transformers, FAISS, scikit-learn
- Task Queue: Celery
- Container: Docker, GitHub Container Registry
Landing Page |
| # | Team Member | Role | GitHub Profile |
|---|---|---|---|
| 1 | Fareed Ahmed Owais | 🎯 Team Lead | 🔗 FareedAhmedOwais |
| 2 | Abdur Rahman Qasim | 🔎 Research Engineer | 🔗 Abdur-rahman-01 |
| 3 | Mohammed Saad Uddin | 🚀 Full-stack + AI/ML Developer | 🔗 saad2134 |
This project is licensed under the MIT License - see the LICENSE file for details.
- ✅ Commercial use
- ✅ Modification
- ✅ Distribution
- ✅ Private use
- ❌ Liability
- ❌ Warranty
Developed with 💖 for the AMD Slingshot Hackathon 2026, with heartfelt thanks for the opportunity to build and innovate.
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