Why AI matters at this scale

The University of Washington's Department of American Ethnic Studies (AES) is a large academic unit within a major public research university. Founded in 1985, it focuses on the interdisciplinary study of race, ethnicity, and indigeneity, producing scholarly research, educating thousands of students, and engaging with communities. At this scale—serving a vast student body and managing extensive research archives—manual processes for research, content curation, and administration create bottlenecks. AI presents tools to amplify scholarly impact, personalize education at scale, and preserve cultural heritage more efficiently, allowing the department to further its mission despite finite resources.

Concrete AI Opportunities with ROI

1. Augmented Scholarly Research: Faculty and graduate students spend countless hours sifting through archival documents, oral histories, and multimedia. An AI research assistant can perform semantic search, topic modeling, and network analysis across digitized collections. The ROI is measured in accelerated discovery, enabling the publication of more high-impact research and successful grant proposals, directly benefiting the department's prestige and funding.

2. Dynamic Curriculum Development: The department must constantly update courses to reflect evolving discourses. AI can analyze current publications, news, and digital media to suggest relevant contemporary case studies and primary sources. It can also help audit syllabi for diversity of perspectives. This reduces the manual curation burden on faculty, ensuring courses remain cutting-edge and inclusive, which improves student enrollment and satisfaction.

3. Accessible Archival Preservation: A significant portion of the department's cultural heritage materials—like aging interview tapes—are at risk. AI-driven audio restoration, automated transcription, and translation can digitize and make these resources searchable and accessible. The ROI is the preservation of irreplaceable assets and the unlocking of their value for researchers worldwide, enhancing the department's role as a central scholarly hub.

Deployment Risks Specific to a Large University Department

Implementing AI in a large, bureaucratic public university environment carries distinct risks. Budget Fragmentation: While the overall university is large, discretionary IT funding at the departmental level is often limited and tied to annual cycles, making multi-year AI project investment difficult. Governance and Compliance: Any tool handling sensitive historical data or student information must navigate stringent university-wide data governance, IRB protocols, and potentially public records laws, slowing pilot projects. Academic Culture: Adoption requires buy-in from tenured faculty who may be skeptical of algorithmic methods, especially in a field critically examining systemic bias. Projects must be framed as collaborative tools, not replacements for scholarly expertise. Integration Challenges: New AI tools must interoperate with legacy university systems (e.g., learning management systems, library databases), requiring central IT support that may not be prioritized.