VoyaTutor: Knowledge Graph for Educational Platform

Objective

Engineer a sophisticated graph layer in Neo4j to manage multi-layered educational requirements including phases, units, and concepts.
Map complex pedagogical hierarchies—from basic units to advanced Bloom's Taxonomy skill levels—to identify precise learner needs.
Develop a recommendation system using weighted Cypher queries to suggest personalized content based on the student's current knowledge state.
Build robust API endpoints to expose the knowledge graph for real-time integration into the existing tutor application.

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Overview

Voya Tutor is a next-generation educational platform that leverages graph science to provide personalized learning journeys. By architecting a multi-layered knowledge graph in Neo4j, we transformed fragmented educational content into a relational ecosystem that powers intelligent, mission-critical student recommendations, according to the GCSE program.

Problem

Traditional educational platforms often rely on linear NoSQL or relational databases, which lack the semantic depth required to map complex, multi-dimensional learning paths. This "flat" data structure created a significant bottleneck, making it impossible to provide precise recommendations or clear gap identification, ultimately slowing down student progress and exam readiness.

Results

Successfully replaced rigid data structures with a relational knowledge graph, enabling high-stakes educational personalization.
Delivered a recommendation engine that utilizes weighted graph science to guide students through optimized, data-driven learning paths.
Achieved transparent identification of student knowledge gaps, directly supporting targeted and effective exam preparation.
Validated the system through rigorous test trials and sample data integration, ensuring a production-ready infrastructure for real-world educational impact.

From product architecture to global deployment — we have it covered.

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