Graph+AI

About the Project

In today’s fast-paced world, access to personalized education has become a necessity. Many learners struggle to find clear and efficient paths to gain knowledge, often getting overwhelmed by the abundance of resources and unclear prerequisites. This inspired us to create Personalized Learning Pathways, a model-native app that leverages the power of knowledge graphs and AI to provide tailored learning journeys for users.

Inspiration

The idea was born out of a common challenge we observed: the lack of structured guidance for learners exploring new domains. Whether it’s learning programming, mastering a language, or diving into advanced topics like machine learning, many learners give up due to the absence of clear directions. By combining knowledge graphs with AI, we aimed to address this gap and create an intelligent learning assistant.

What We Learned

Throughout this project, we gained valuable insights into:

Knowledge Graphs: Understanding how entities and relationships can model real-world learning pathways.

Graph Databases (Neo4j): The importance of efficient storage and querying for interconnected data.

API Design: Building modular and scalable APIs with the Modus API framework.

AI Integration: Implementing recommendation systems powered by AI to enhance user experience.

User-Centric Design: The importance of building intuitive interfaces that cater to diverse learners.

How We Built the Project

Knowledge Graph Construction:

Designed a schema with entities such as Topics, Resources, and Users, and relationships like PREREQUISITES and HAS_RESOURCE.

Stored the graph in Neo4j, enabling efficient traversal and queries.

Modus API Integration:

Built RESTful APIs to interact with the knowledge graph, allowing users to fetch topics, update progress, and get personalized recommendations.

AI for Recommendations:

Implemented logic to recommend the next topics based on user progress and preferences.

Used GPT models to enable natural language search and contextual suggestions.

Frontend Development:

Designed a user-friendly dashboard to visualize learning pathways, track progress, and explore resources.

Challenges We Faced

Complexity in Graph Modeling: Structuring topics, prerequisites, and resources in a meaningful way that scales as the graph grows.

Integration of AI: Balancing AI capabilities with graph-based logic to deliver accurate recommendations.

User Personalization: Designing an algorithm that considers diverse user preferences and learning styles.

Time Constraints: Building and testing a fully functional prototype within a limited timeframe.

The Result

The final product is a functional prototype that empowers users to:

Explore topics through a visual knowledge graph.

Receive personalized learning recommendations.

Track progress seamlessly and stay motivated.

Built With

• ai
• ec2:
• fastapi:
• neo4j:
• python: