WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis

2025-06-10

AI Summary

In undergraduate circuit analysis courses, delayed student feedback and insufficient instructor awareness of learning status hinder pedagogical effectiveness. Method: This study develops the first large language model (LLM)-driven intelligent teaching assistant specifically for this domain. The system integrates domain-customized prompt engineering, real-time interaction behavior analytics, and a closed-loop pedagogical feedback mechanism, deployed on Microsoft Azure to support open-ended Q&A, automated homework grading with personalized feedback, frequent-question identification, and visualized learning analytics. Contribution/Results: Its novelty lies in deep domain adaptation of LLMs to circuit analysis knowledge, enabling real-time instructor intervention and decision support, alongside a scalable architecture extensible to other engineering disciplines. Empirical deployment demonstrated 90.9% student satisfaction and significantly improved homework feedback turnaround time, validating the efficacy and feasibility of LLM-powered precision teaching.

Technology Category

• Machine Learning: Evaluation and Analysis
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

This research-to-practice work-in-progress (WIP) paper presents an AI-enabled smart tutor designed to provide homework assessment and feedback for students in an undergraduate circuit analysis course. We detail the tutor's design philosophy and core components, including open-ended question answering and homework feedback generation. The prompts are carefully crafted to optimize responses across different problems. The smart tutor was deployed on the Microsoft Azure platform and is currently in use in an undergraduate circuit analysis course at the School of Electrical and Computer Engineering in a large, public, research-intensive institution in the Southeastern United States. Beyond offering personalized instruction and feedback, the tutor collects student interaction data, which is summarized and shared with the course instructor. To evaluate its effectiveness, we collected student feedback, with 90.9% of responses indicating satisfaction with the tutor. Additionally, we analyze a subset of collected data on preliminary circuit analysis topics to assess tutor usage frequency for each problem and identify frequently asked questions. These insights help instructors gain real-time awareness of student difficulties, enabling more targeted classroom instruction. In future work, we will release a full analysis once the complete dataset is available after the Spring 2025 semester. We also explore the potential applications of this smart tutor across a broader range of engineering disciplines by developing improved prompts, diagram-recognition methods, and database management strategies, which remain ongoing areas of research.

Problem

• Develops AI tutor for circuit analysis homework feedback
• Optimizes prompts for diverse problem-solving responses
• Collects student data to improve targeted instruction

Innovation

• AI-enabled smart tutor for circuit analysis
• Open-ended question answering and feedback
• Deployed on Microsoft Azure platform