Why AI matters at this scale

Washington State University's Department of Mathematics and Statistics is a large academic unit within a major public research university. It educates thousands of students across foundational and advanced courses in mathematics and statistics, supports graduate programs, and conducts significant research. At this scale—serving a student body in the 10001+ size band—manual processes for instruction, grading, and student support become inefficient and limit personalization. AI presents a critical lever to enhance educational outcomes and research productivity while managing costs, a priority given public funding constraints.

Concrete AI Opportunities with ROI Framing

1. Personalized Learning at Scale: Implementing AI-driven adaptive learning platforms in large introductory courses (e.g., Calculus, Statistics) can dynamically adjust content difficulty and provide tailored feedback. The ROI is direct: improved student pass rates and retention increase tuition revenue and state funding metrics tied to student success, while reducing the need for costly remedial instruction.

2. Automating High-Volume Tasks: AI-powered tools for grading structured homework and quizzes in core courses can provide instant feedback. This saves graduate teaching assistants and faculty hundreds of hours per semester. The ROI is calculated through labor cost avoidance and the reallocation of expert time to higher-value activities like research, mentorship, and complex instruction, boosting the department's research output and prestige.

3. Augmenting Research Capability: For a department with strong statistics and data science research, AI can accelerate data analysis, simulation, and model development. AI-assisted research tools can help faculty and graduate students tackle more complex problems faster. The ROI manifests in increased grant funding, higher publication rates, and enhanced ability to attract top-tier graduate students and faculty, strengthening the department's academic standing.

Deployment Risks Specific to Large Public Universities

Deploying AI in a large, decentralized public university environment carries specific risks. Integration Complexity is high, as new AI tools must interoperate with entrenched legacy systems (student information systems, LMS like Canvas) and diverse departmental workflows. Change Management across a large, tenured faculty with varying digital fluency can slow adoption; securing buy-in requires demonstrating clear pedagogical benefit, not just efficiency. Regulatory and Ethical Scrutiny is intense; AI use with student data must navigate FERPA, state regulations, and ethical concerns around algorithmic bias in grading or advising, requiring robust governance. Finally, Funding Cycles for public institutions can be inflexible, making large upfront investments in AI infrastructure challenging, often necessitating a phased, grant-funded approach.