How Universities Decide Which Small Courses to Cut: The Growing Role of Data and AI

Universities are facing an increasingly visible challenge: the number of students enrolled in certain courses varies enormously from year to year and from section to section. According to Inside Higher Ed, this situation leads to difficult decisions about whether to keep or cut sections with low enrollment.

The problem of small sections

In an article by Matt Reed, it's noted that "enrollment shifts aren't evenly distributed," meaning changes in enrollment numbers don't follow a predictable pattern. This means some course sections end up with very few students, while others become overcrowded. For university administrators, this imbalance complicates budget planning and the allocation of teaching resources.

Traditionally, decisions about cutting small sections were made manually, based on administrators' experience and limited historical data. But the growing complexity of higher education systems is making these methods increasingly inadequate.

Where artificial intelligence comes in

More and more institutions are exploring the use of AI-based tools to analyze historical enrollment patterns, anticipate fluctuations, and provide more precise recommendations on which sections are worth keeping. Algorithms can identify subtle correlations - for example, the link between a course's schedule, the instructor teaching it, and the number of students enrolled - things that are hard to detect through manual analysis alone.

These systems promise more efficient resource allocation, but they also raise legitimate questions. How much should algorithms influence decisions that directly affect students' educational experience? There's a risk that numerical efficiency could take precedence over the real needs of a diverse academic community.

Balancing efficiency and quality

Experts cited by Inside Higher Ed warn that fully automating these decisions could harm niche courses that are essential to certain degree programs, even if they attract few students. The challenge for universities is to use AI tools as decision-support aids, not as a replacement for human judgment and long-term educational values.

As technology advances, dialogue between administrators, faculty, and AI solution developers will become essential to finding a sustainable balance between operational efficiency and the quality of education.