Toward Personalizing Quantum Computing Education: An Evolutionary LLM-Powered Approach

Quantum computing education faces significant challenges due to its complexity and the limitations of current tools. This thesis introduces a novel Intelligent Teaching Assistant for quantum computing education and details its evolutionary design process. The system combines a knowledge-graph-augmented architecture with two specialized LLM agents: a Teaching Agent for dynamic interaction and a Lesson Planning Agent for lesson generation. The system is designed to adapt to individual student needs, with interactions meticulously tracked and stored in a knowledge graph. This graph represents student actions, learning resources, and their relationships, aiming to enable reasoning about effective learning pathways. We describe the implementation of the system, highlighting the challenges encountered and the solutions implemented, including a dual agent architecture where tasks are separated, coordination through a central knowledge graph that maintains system awareness, and a user-facing tag system intended to mitigate LLM hallucination and improve user control. Preliminary results from simulated runs illustrate the system’s potential to capture rich interaction data, dynamically adapt lesson plans based on student feedback, and facilitate context-aware tutoring, though systematic evaluation with human learners is required to validate these capabilities.