Personal Learning Manager (PLM)
Agentic AI-Powered Learning System for self-regulated, adaptive education.
Overview
Traditional study tools treat a 50-page reading and a 10-minute quiz the same way, offering no real help with the actual bottleneck of self-regulated learning: organizing, digesting, and scheduling material.
The Personal Learning Manager (PLM) closes this gap by transforming static study materials into dynamic, personalized learning experiences. By combining Agentic AI workflows with Retrieval-Augmented Generation (RAG), the system acts as a persistent learning companion.
Designed specifically for students managing heavy academic loads, PLM bridges the gap between active study time and everyday constraints, making personalized learning practical.
Key Architecture & Features
PLM integrates powerful AI agents that execute complex multi-step workflows. Uploaded documents are parsed, indexed in a PostgreSQL database with pgvector, and queried dynamically using RAG techniques to construct a custom knowledge base.
An automation layer powered by n8n orchestrates background workflows, coordinating between embedding models, large language models (LLMs), and external services like the Google Calendar API for adaptive scheduling.
Learning Space & RAG Assistant
Organize documents into dedicated subject spaces. Ask questions, draft summaries, and retrieve information with a context-aware AI tutor.
Adaptive Learning Paths
AI analyzes course syllabi to generate step-by-step learning paths that adjust automatically based on quiz performance.
Active Recall & Quizzing
Automatically generates personalized, concept-based quizzes to test retention, reinforcing weak areas on the fly.
Adaptive Scheduling & Calendar Sync
Calculates required study hours and syncs events with Google Calendar, shifting plans automatically if real-world schedules change.
Project Status & Target Outcomes
Active Dev
Status
Gemini/OpenAI
AI Backend
n8n Agent
Orchestration
Currently under active development, this system is being built to serve as a research prototype exploring self-regulated learning frameworks powered by AI.
The goal is to test the efficacy of Agentic AI in helping students reduce study-planning overhead, increase retention through active recall, and maintain consistent study habits.