CircuTutor turns static circuit problems into interactive learning tools
CircuTutor: Transforming Static Circuit Problems into Intelligent and Dynamic Tutoring
Artificial Intelligence
Summary
Understanding how electric circuits work can be tricky because you can’t see things like voltage or current directly. CircuTutor helps by turning paper-based circuit problems into interactive lessons where students can change parts of the circuit and get instant feedback. It shows animations of what happens inside the circuit, explains why certain answers are right or wrong, and suggests extra practice for areas where the student struggles. The authors show that this approach makes learning circuit concepts clearer and more engaging. This method could also be useful for teaching other science and engineering topics.
direct currentcircuit simulationcircuit topologySPICE solverinteractive tutoringconceptual learningcausal reasoningmisconception diagnosismultimodal parsingSTEM education
Authors
Ziyu Luo, Xiaorui Ma, Lin Chen, Xiaoming Chen
Abstract
Learning direct current circuit concepts requires learners to connect invisible physical quantities, such as current, voltage, resistance, and power, with observable outcomes such as bulb brightness. Conventional textbook materials and general-purpose circuit simulators provide opportunities for problem solving and exploration but offer limited support for explaining why circuit behavior changes or diagnosing the reasoning behind incorrect answers. We present CircuTutor, a circuit-state-driven intelligent tutoring system that transforms static textbook circuit problems into an interactive tutoring workflow. CircuTutor first uses multimodal problem parsing to extract the textbook question, circuit topology, component parameters, switch states, and answer options, which are converted into a structured task and validated through circuit simulation. Learners can then interactively explore the circuit (by changing parameters) and submit an answer while a SPICE-compatible solver computes physically consistent circuit states. After the learner submits an answer, CircuTutor presents a before-and-after circuit state animation corresponding to the selected operation, organizes the simulated state changes into a causal reasoning chain that explains the underlying circuit behavior, maps answer discrepancies to likely misconceptions, and generates adaptive follow-up exercises targeted at the diagnosed misconception. Our experimental results demonstrate that CircuTutor effectively improves conceptual learning and the overall learning experience. The proposed framework demonstrates how simulated circuit states can be transformed into intelligent and interactive tutoring for circuit education, with the potential to generalize to other STEM domains.