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 paper introduces a novel Intelligent T eaching Assistant for quantum computing education and details its evolutionary design process. The system combines a knowledge-graph-augmented architecture with two specialized Large Language Model (LLM) agents: a T eaching Agent for dynamic interaction, and a Lesson Planning Agent for lesson plan 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 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 introducing a dual-agent architecture where tasks are separated, all coordinated 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 illustrate the system's potential to capture rich interaction data, dynamically adapt lesson plans based on student feedback via a tag system in simulation, and facilitate context-aware tutoring through the integrated knowledge graph, though systematic evaluation is required. Quantum computing offers a revolutionary paradigm shift, but a significant workforce gap hinders its progress [1]. Teaching quantum computing is uniquely challenging, demanding an interdisciplinary understanding of physics, computer science, and mathematics, compounded by the counterintuitive nature of quantum principles. Traditional teaching methods and tools often fail, one of the many reasons is students' diverse background [2]. On the other hand, novel methods and tools based on generative artificial intelligence are still unproven in terms of successful teaching practices and quantifiable results.