Why AI matters at this scale

The University of Dayton's School of Education and Health Sciences (SEHS) Online division operates at a pivotal scale. With an estimated 501-1,000 employees, it is large enough to have significant administrative overhead and student support needs, yet small and focused enough to implement new technologies without the extreme inertia of a massive university system. In the competitive and rapidly evolving online education sector, AI is not merely a luxury but a strategic lever for differentiation. It enables this mid-sized player to deliver the personalized attention and operational efficiency typically associated with smaller institutions, while leveraging the data and resources of a larger organization. For a unit focused on professional and continuing education, where student outcomes, retention, and career relevance are paramount, AI tools can directly enhance the core educational mission and business model.

Concrete AI Opportunities with ROI Framing

1. Adaptive Learning Platforms for Improved Completion Rates: A primary ROI challenge in online education is student attrition. Deploying an AI-driven adaptive learning platform within the Learning Management System (LMS) can create personalized learning paths. By analyzing interaction data and assessment performance, the system dynamically adjusts content difficulty and provides targeted resources. The direct ROI is measured through increased course completion and program persistence rates, leading to higher tuition revenue and improved institutional rankings. The investment is justified by reducing the cost of recruiting new students to replace those who drop out.

2. AI-Powered Student Support and Advising: Scaling high-touch support for hundreds of online students is costly. An intelligent chatbot and virtual assistant can handle routine inquiries 24/7, from registration deadlines to financial aid questions, and perform initial academic advising checks. This frees professional advisors to focus on complex, high-need cases. The ROI is clear: it increases the effective student-to-advisor ratio without degrading service quality, controlling staffing costs while potentially improving student satisfaction scores (e.g., NPS), which drives referrals.

3. Predictive Analytics for Proactive Intervention: Reactive support often fails at-risk students. Machine learning models can synthesize data from the LMS, student information system, and even communication platforms to generate early alerts for students showing signs of struggle (e.g., declining login frequency, low quiz scores). Advisors can then intervene proactively. The financial ROI is tied directly to retaining tuition from students who would otherwise withdraw. An ancillary benefit is the positive impact on graduation rates, a key metric for institutional accreditation and reputation.

Deployment Risks Specific to This Size Band

For an organization of this size, specific risks must be managed. Resource Allocation is a primary concern: investing in AI may compete with other critical IT or faculty development projects. A clear, phased pilot approach is essential to demonstrate value before full-scale rollout. Integration Complexity is another hurdle. The tech stack likely includes an LMS, CRM, and SIS from different vendors. Ensuring AI tools can seamlessly access and process data across these silos requires upfront technical scoping and potentially middleware investments. Finally, Change Management is critical. With a workforce spanning faculty, administrators, and IT staff, securing buy-in is necessary to overcome skepticism. A transparent strategy that positions AI as an augmentative tool for enhancing human roles, rather than replacing them, is vital for successful adoption and realizing the projected ROI.