Main Elements & Technologies

Data Intake: Uploads of text, transcripts, logs, and documents.

Insight Engine: NLP-based concept extraction, knowledge-gap detection, and summaries.

Reference: https://www.sciencedirect.com/science/article/pii/S0360131520302075

Automation Tools: Auto-generated quizzes, feedback, and study materials.

Reference: https://dl.acm.org/doi/10.1145/3287324.3287481

Dashboards: Trend visualizations and student progress insights.

Technologies: NLP models, Python/Node.js backend, React frontend, PostgreSQL, vector search.

Objectives

Improve digital learning efficiency, reduce repetitive manual tasks, and deliver adaptive materials based on real student data.

Short Implementation Plan

Phase 1: Core architecture, data intake, basic dashboard.

Phase 2: Insight engine for analysis and clustering.

Phase 3: Automation features (quizzes, feedback).

Phase 4: Pilot testing and refinement.