Recognition, Retrieval, and Response: The AITEE Framework for Socratic Tutoring in Electrical Engineering
Christopher Knievel, Alexander Bernhardt, Christian Bernhardt · IEEE Access · 2026
Intelligent tutoring systems combined with large language models offer a promising approach to addressing the teacher bandwidth problem in electrical engineering education. However, standard text-based retrieval fails in symbolic domains where topology dictates solution methodology. In this paper, we present AITEE, an agentic tutoring framework that addresses three fundamental challenges: robust perception of hand-drawn circuits, structure-aware retrieval in symbolic domains, and pedagogically sound dialogue generation. Our circuit recognition pipeline processes informal sketches with high fidelity through a novel line-loss metric for connection validation. To overcome the limitations of text-based similarity in circuit analysis, we propose a structure-aware Multi-Representation Indexing (MRI), which uses Graph Neural Networks to generate topology-based embeddings that retrieve methodologically relevant content based on structural identity rather than textual overlap. The system enforces a strict separation between conversational reasoning and numerical computation, delegating arithmetic verification to SPICE simulation to eliminate calculation hallucinations. Experimental evaluations demonstrate that MRI enables even medium-sized open-source models (70B parameters) to achieve competitive performance on complex circuit topologies where advanced text-based retrieval methods fail, reaching 85% accuracy in methodology application. Qualitative analysis reveals that while instruction-prompted models successfully maintain Socratic dialogue, rigid inquiry-based approaches can impose excessive cognitive load on novices, suggesting that effective AI tutoring requires adaptive scaffolding to balance learner autonomy with necessary cognitive support.