Why AI matters at this scale

Rutgers School of Arts and Sciences (SAS) is the academic heart of Rutgers University–New Brunswick, a major public research institution. As the largest school within the university, SAS encompasses a vast array of disciplines in the humanities, social sciences, and natural sciences, serving thousands of undergraduate and graduate students with a faculty engaged in cutting-edge research. Its mission is to provide a foundational liberal arts education while advancing knowledge through scholarship. At this scale—over 10,000 individuals—manual processes and one-size-fits-all approaches struggle to meet the needs of a diverse student body and ambitious research enterprise.

AI is a transformative lever for an institution of this size and complexity. It moves beyond administrative efficiency to core academic and research missions. For a public university, pressures to improve graduation rates, ensure equitable outcomes, and maximize research impact are intense. AI provides the tools to analyze patterns across massive datasets, predict outcomes, personalize interactions, and automate routine tasks at a scale impossible for human staff alone. This allows SAS to allocate its human capital—faculty, advisors, researchers—to high-touch, high-value activities where they matter most.

Concrete AI Opportunities with ROI Framing

1. Predictive Analytics for Student Retention

Deploying machine learning models on historical student data can identify early warning signs of academic difficulty or disengagement. The ROI is direct: each retained student represents preserved tuition revenue and improved graduation metrics, which are critical for state funding and rankings. A modest percentage increase in retention can translate to millions in revenue and significantly advance equity goals.

2. AI-Augmented Research and Grant Acquisition

Faculty time is a premium resource. AI tools can streamline literature reviews, suggest experimental designs, and assist in drafting and polishing grant proposals tailored to specific funders. The ROI is measured in increased grant submission rates, higher award probabilities, and freed-up faculty time for actual research, directly boosting the school's research prestige and indirect cost recovery.

3. Intelligent Curriculum and Resource Optimization

AI can analyze decades of course enrollment data, student pathway success, and workforce trends to recommend curriculum updates and optimize class scheduling. This ensures high-demand courses are available, reduces classroom waste, and aligns offerings with student goals. The ROI comes from better space utilization, improved student time-to-degree, and enhanced student satisfaction.

Deployment Risks Specific to This Size Band

For an organization with 10,000+ people, change management is the paramount risk. Rolling out AI tools requires buy-in from a decentralized body of tenured faculty, unionized staff, and a large IT department with legacy systems. Data silos are significant; student information, research data, and financial systems may reside on different, incompatible platforms. The scale also magnifies ethical and compliance risks. Any algorithmic bias in admissions, advising, or grading could affect thousands of students and trigger regulatory (FERPA) and reputational crises. Implementation must be phased, involve extensive stakeholder communication, and prioritize transparency and fairness to mitigate these large-institution risks.