用课程记录和专业计划做智能推荐，帮本科生科学选课
SmartCourse: A Contextual AI-Powered Course Advising System for Undergraduates
- 结合成绩单和培养方案，生成个性化选课建议
- 实测显示完整上下文推荐相关性提升显著
- 适合需要精准学业规划的本科生和教师使用
我们提出 SmartCourse，一个面向本科生（特别是计算机科学专业）的集成化课程管理与AI驱动的学业指导系统。该系统克服了传统指导工具的局限，通过整合学生个人成绩单和培养方案信息，提供上下文感知的建议。系统支持命令行界面（CLI）和Gradio网页前端，实现用户管理、课程注册、成绩记录及四年制学位计划管理，并集成本地部署的大语言模型（通过Ollama）进行个性化课程推荐。系统基于成绩单和专业计划，可提供如优先满足必修要求或重修建议等上下文相关指导。我们在25个代表性咨询问题上评估系统，引入自定义指标：PlanScore、PersonalScore、Lift和Recall，以衡量不同上下文条件下的推荐质量。实验表明，使用完整上下文的推荐显著优于忽略上下文的模式，证实了成绩单与培养方案信息在个性化学业指导中的必要性。SmartCourse展示了基于成绩单感知的AI如何提升学术规划效率。
原文摘要 · Abstract (English)
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.
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