SmartCourse: A Contextual AI-Powered Course Advising System for Undergraduates

📅 2025-07-26
📈 Citations: 0
Influential: 0
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🤖 AI Summary
Traditional course advising tools struggle to integrate individual student profiles—such as transcripts and major curricula—resulting in limited personalization. This paper proposes a transcript-aware intelligent course advisor system tailored for undergraduate computer science students, integrating academic records with degree requirements and supporting both CLI and web interfaces for course recommendation, grade tracking, and four-year academic planning. We introduce a context-aware mechanism and novel evaluation metrics—including PlanScore, PersonalScore, Lift, and Recall—to quantify recommendation quality. The system employs a locally deployed large language model (via Ollama) as its AI engine, tightly coupled with the institutional course management system. Evaluation across 25 representative advising scenarios demonstrates that full contextual integration significantly improves recommendation relevance, validating its critical value for academic advising.

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📝 Abstract
We present SmartCourse, an integrated course management and AI-driven advising system for undergraduate students (specifically tailored to the Computer Science (CPS) major). SmartCourse addresses the limitations of traditional advising tools by integrating transcript and plan information for student-specific context. The system combines a command-line interface (CLI) and a Gradio web GUI for instructors and students, manages user accounts, course enrollment, grading, and four-year degree plans, and integrates a locally hosted large language model (via Ollama) for personalized course recommendations. It leverages transcript and major plan to offer contextual advice (e.g., prioritizing requirements or retakes). We evaluated the system on 25 representative advising queries and introduced custom metrics: PlanScore, PersonalScore, Lift, and Recall to assess recommendation quality across different context conditions. Experiments show that using full context yields substantially more relevant recommendations than context-omitted modes, confirming the necessity of transcript and plan information for personalized academic advising. SmartCourse thus demonstrates how transcript-aware AI can enhance academic planning.
Problem

Research questions and friction points this paper is trying to address.

Addresses limitations of traditional advising tools for undergraduates
Integrates transcript and plan data for personalized course recommendations
Evaluates AI-driven advising quality with custom metrics and context
Innovation

Methods, ideas, or system contributions that make the work stand out.

Integrates transcript and plan for student context
Uses CLI and Gradio GUI for user interaction
Leverages local LLM for personalized recommendations
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Yixuan Mi
Department of Computer Science, Wenzhou-Kean University, Wenzhou, China; Department of Computer Science, Kean University, Union, NJ, United States
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Yiduo Yu
Department of Computer Science, Wenzhou-Kean University, Wenzhou, China; Department of Computer Science, Kean University, Union, NJ, United States
Y
Yiyi Zhao
Department of Computer Science, Wenzhou-Kean University, Wenzhou, China; Department of Computer Science, Kean University, Union, NJ, United States