System Architecture Overview

The Neuro-Symbolic Approach

EduVision ITS represents a significant leap forward in educational technology by adopting a Neuro-Symbolic AI Architecture. Unlike traditional systems that rely solely on heuristic rules (Symbolic) or purely on black-box Large Language Models (Neural), EduVision synthesizes both approaches to achieve:

1. Precision & Control: Symbolic logic (Knowledge Graphs, BKT) ensures the system follows pedagogical rules and tracks mastery accurately.
2. Flexibility & Empathy: Neural networks (LLMs via Together AI) provide natural language understanding, dynamic explanation generation, and conversational adaptability.

The 5-Engine Core

The system is composed of five distinct, decoupled engines that interact asynchronously.

graph TD
    subgraph "Cognitive Core"
        LE[📈 Learner Engine]
        PE[🧠 Pedagogy Engine]
        KE[🗂️ Knowledge Engine]
    end

    subgraph "Interaction Layer"
        TE[💬 Tutor Engine]
        AE[✅ Assessment Engine]
    end

    Student((👨‍🎓 Student)) <--> TE
    Student --> AE
    
    TE <--> PE
    PE <--> LE
    PE <--> KE
    AE --> LE

1. 🗂️ Knowledge Engine (The "Cortex")

• Responsibility: Stores and retrieves domain knowledge.
• Technology: PostgreSQL + pgvector (for semantic search) + Knowledge Graph (for structural relationships).
• Function: It ingests raw content (PDFs, text), chunks them into learnable units, and generates embeddings using sentence-transformers/all-MiniLM-L6-v2. It powers the RAG (Retrieval-Augmented Generation) pipeline.

2. 🧠 Pedagogy Engine (The "Strategist")

• Responsibility: Decision making.
• Technology: Reinforcement Learning / Rule-Based Heuristics (Vygotsky's ZPD).
• Function: It decides what to teach next. It balances the difficulty of the material against the student's current ability level to maintain flow. Strategies include Socratic questioning, Scaffolding, and Feynman techniques.

3. 📈 Learner Engine (The "Hippocampus")

• Responsibility: Long-term memory and state tracking.
• Technology: Bayesian Knowledge Tracing (BKT) + Spaced Repetition (SRS).
• Function: It maintains a probabilistic model of the student's mastery for every concept. It predicts forgetting curves and schedules reviews, persisting state in PostgreSQL.

4. 💬 Tutor Engine (The "Voice")

• Responsibility: Natural language generation.
• Technology: Meta Llama 3.1 (8B Instruct Turbo) via Together AI API.
• Function: It translates the Pedagogy Engine's strategy into human-like dialogue. It can explain, hint, encourage, and question the student using Socratic methods. It is strictly prompted to be an educational guide, not just a chatbot.

5. ✅ Assessment Engine (The "Judge")

• Responsibility: Evaluation and feedback.
• Technology: AST Analysis (for Code), Semantic Similarity (for Text), LLM-based Grading.
• Function: It grades student inputs (code, essays, answers) objectively and provides structured feedback to the Learner Engine.

Data Flow Lifecycle

1. Ingestion: Teacher uploads material -> Knowledge Engine structures it & Vectorizes it.
2. Session Start: Student logs in -> Learner Engine retrieves profile -> Pedagogy Engine selects initial topic.
3. Interaction: Tutor Engine presents topic -> Student responds.
4. Assessment: Assessment Engine evaluates response -> Updates Learner Engine.
5. Adaptation: Learner Engine updates mastery -> Pedagogy Engine adjusts strategy (e.g., move to next topic or remediate).