Governing AI in Education: A Real-World Use Case for Predicting Student Enrollment
Higher education institutions are increasingly turning to AI to support strategic planning. One area where this is especially relevant is enrollment forecasting. Predicting how many students will enroll in a given term affects everything from staffing and budgeting to housing and course offerings. But as models become more complex, so does the need to manage them responsibly.
This post explores a real-world use case where an AI Decision Tree model is used to forecast student enrollment and how governance tools help ensure the model remains reliable, transparent, and aligned with institutional goals.
The Use Case: Forecasting Enrollment with Decision Trees
Enrollment forecasting has always been a high-stakes task. Institutions need to anticipate demand across programs, campuses, and student demographics. Traditional methods like linear regression or year-over-year comparisons often fall short when patterns shift due to external factors like economic changes, policy shifts, or evolving student preferences.
In this case, a university uses a Decision Tree model to predict enrollment. The model draws on a variety of data sources, including:
- Historical enrollment by program and term
- Demographic data (e.g., age, location, ethnicity)
- Application funnel metrics (e.g., application volume, yield rates)
- External indicators (e.g., unemployment rates, tuition changes)
The model outputs enrollment projections at multiple levels: college, department, and program, which are then used to inform decisions like:
- Course scheduling and faculty hiring
- Financial aid planning
- Marketing and recruitment strategies
Decision Trees are particularly useful here because they’re interpretable. Stakeholders can follow the logic behind a prediction, which helps build trust in the model’s outputs.
Why Governance Is Necessary
Even when a model performs well, it’s important to ask:
- How do we know it’s still working as intended?
- What happens if the data changes?
- Could the model unintentionally introduce bias?
These questions highlight the need for model governance. Without it, institutions risk making decisions based on outdated, opaque, or unfair models.
Governance isn’t just about compliance it’s about ensuring that models remain useful, understandable, and aligned with institutional values. In this case, the university uses a governance framework to manage the model throughout its lifecycle.
Governing the Model: A Lifecycle Approach
Here’s how the university team governs the Decision Tree model from initial assessment to retirement:
1. Model Candidate Assessment
Before the candidate is approved for use, it goes through an initial evaluation:
- Is the model solving a clearly defined problem?
- Are the data inputs reliable and representative?
- Does the model meet baseline performance and interpretability standards?
- Is the candidate machine based?
This step helps determine whether the model is suitable for deployment and long-term governance.
2. Documentation and Registration
Once approved, you can:
- Register the candidate in a central inventory
- Document with metadata: linkages to other objects, documentation links, inputs, outputs, assumptions, training data, and version history
This ensures transparency and makes it easier for others to understand and review the model later.
3. Validation and Audit
Before deployment, the model undergoes independent validation:
- Performance testing on holdout datasets
- Bias and fairness checks across demographic groups
- Sensitivity analysis to understand how input changes affect predictions
Findings are logged, and the model is either approved or sent back for revision.
4. Deployment and Monitoring
Once deployed, the model is monitored continuously:
- Drift detection: Is the model still performing well as new data comes in?
- Alerting: If accuracy drops or bias emerges, alerts are triggered
- Usage tracking: Who is using the model, and how often?
Monitoring ensures the model remains reliable and relevant over time.
5. Dashboard Reporting
Governance dashboards provide:
- A snapshot of model health (accuracy, fairness, usage)
- Audit trail visibility (who changed what and when)
- Risk indicators (e.g., models nearing performance thresholds)
These reports help leadership stay informed and support decision-making.
6. Model Retirement or Redevelopment
Eventually, the model may be retired or replaced:
- If performance declines and retraining doesn’t help
- If the business need changes
- If a better modeling approach becomes available
Retirement is documented, and the model is archived with its full history for future reference.
Tools That Support the Process
To support this lifecycle, the university uses a model management platform that helps coordinate documentation, validation, monitoring, and reporting. While the platform itself isn’t the focus, it plays a key role in helping teams stay organized and accountable.
Rather than replacing human judgment, governance tools provide structure and visibility. They help ensure that models are not just technically sound, but also operationally sustainable.
Lessons for Other Institutions
This use case offers a few takeaways for institutions exploring AI in strategic planning:
- Interpretability matters: Decision Trees offer a balance between predictive power and explainability, which is important when decisions affect real people.
- Governance is ongoing: A model that works today may not work tomorrow. Regular monitoring and review are essential.
- Tools support, not replace, governance: Platforms help manage complexity, but governance still requires thoughtful human oversight.
Final Thoughts
AI can be a powerful tool for higher education, especially when used to support complex planning tasks like enrollment forecasting. But predictive models are not “set it and forget it” solutions. They need to be governed just like any other critical system.
By combining interpretable models with structured governance practices, institutions can make better decisions while maintaining transparency and trust. Whether you're just starting to explore AI or already managing a portfolio of models, this use case shows that responsible AI is not just possible it’s practical.
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