用AI分析课程设置，帮学生更快毕业
Analyzing Curricular Pattern Complexity Using AI to Improve On-Time Graduation Rates
- 用大语言模型分析课程顺序问题
- 识别导致延迟毕业的关键课程节点
- 适合教育管理者和课程设计者参考
人工智能的兴起使大规模数据的自动分析成为可能，以往耗时耗力的任务如今可高效完成。本文利用AI技术分析并优化软件工程本科课程体系。课程通常存在长序列依赖，若某门课未通过，可能导致四年内无法毕业。传统上由教师手动分析修订课程，过程冗长且更新缓慢，难以适应学生需求变化。本文通过大语言模型（LLMs）分析课程模式，提出改进建议，显著缩短课程调整周期，减少瓶颈环节，缓解毕业延迟问题。
原文摘要 · Abstract (English)
The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI. This work uses AI techniques to analyze and revise curricular patterns in an undergraduate degree for Software Engineering. Curricula often have long sequences where failure to pass a class within the sequence may jeopardize completion of the degree within four years. Manual analysis and revision of curricula by university faculty is a lengthy and labor-intensive process, causing changes to occur rarely and making it impossible to keep up with the changing needs of students. This work reduces the time-to-change for curricula and reduces bottlenecks and graduation delays by using Large Language Models (LLMs) to analyze curricular patterns and suggest revisions.
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