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This study employs Large Language Models (LLMs) to analyze and optimize curricular patterns in undergraduate Software Engineering programs, addressing the inefficiencies of manual curriculum revision processes. By automating the analysis, the research significantly reduces the time required to implement changes, thereby alleviating bottlenecks that can delay student graduation. The key finding indicates that AI-driven curricular revisions can enhance on-time graduation rates by streamlining the identification of problematic course sequences.
AI-driven analysis can drastically cut down curriculum revision time, potentially transforming graduation rates for Software Engineering students.
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.
