The AI Curriculum Corps (AICC) has accepted more than 80 faculty proposals for its inaugural cohort, drawing participation from instructors across WashU. Together, these proposals reflect broad interest in incorporating AI into teaching and learning and examining its implications within specific disciplines.

Most proposals focus on integrating AI into individual assignments (Tier 1) that will be piloted in courses this fall, while others involve designing or redesigning entire courses (Tier 2) that will begin to enroll students in Spring 2027 pending curriculum review committee timelines. The cohort highlights a wide range of approaches and perspectives, positioning AI as both a tool for curricular innovation and a subject of critical inquiry.

The AI Curriculum Corps is a central component of the provost’s +AI initiative, which aims to prepare students for a world increasingly shaped by these technologies. As AI continues to reshape the job market and the skills employers expect from graduates, the courses and assignments developed through this program will help students engage thoughtfully with these tools and grapple with their ethical, social, and disciplinary implications.

Who’s in the room

The program includes faculty from Arts & Sciences, McKelvey Engineering, Olin Business School, the Brown School, Sam Fox School, School of Law, the School of Medicine, the School of Public Health, CAPS and the Beyond Boundaries program.

Participants will be supported with curricular, technical and research support services from the Center for Teaching and Learning, the Digital Intelligence & Innovation (DI2) Accelerator, WashU IT, and WashU Libraries, along with student partners who will provide feedback as assignments and courses are developed.

How faculty are using AI in the curriculum

Across the proposals, a few patterns emerge in how faculty are planning to incorporate AI into their courses.

One cluster of proposals focuses on using AI to provide feedback and make revisions. In these courses, large language models and related tools function less as generators of final products and more as iterative partners. In Julia Deems’s Professional Business Communication course (Olin Business School), for example, AI-driven simulations model how different stakeholders might respond to presentations and written materials, allowing students to make revisions considering the simulated reactions. In McKelvey Engineering, Peizhen Zhu’s Engineering Mathematics course uses AI tools to generate alternative problem-solving approaches and provide feedback, helping students refine their understanding as they work through complex concepts.

A second group treats generative AI itself as an object of critique. Rather than asking what these systems can produce, these assignments ask what’s revealed within their outputs. In Joshua Rusow’s course at the Brown School, students use a generative model to produce life-course predictions based on demographic data, then compare those outputs to real interviews to analyze structural bias. Rebecca Wanzo in Women, Gender, and Sexuality Studies similarly asks students to create public-facing projects that critically interrogate AI’s social impacts (bias, environmental costs, and inequality) through an intersectional, feminist lens. In Arts & Sciences, philosophy professor Anne Baril has students use large language models to generate arguments that contain specific logical fallacies, then analyze and critique those outputs as a way of strengthening their own reasoning.

Other proposals focus on integrating AI into research, coding, analytical, and computational workflows, a domain where these tools are rapidly reshaping professional practice. In Environmental Studies, Christian George introduces students to AI-assisted literature search and summarization, asking them to compare outputs across systems and evaluate accuracy, bias, and omissions. Nan Lin’s AI-Assisted Data Analysis course brings coding agents into the full analytical pipeline, from data cleaning to modeling and interpretation. In McKelvey Engineering, Steve Cole’s Applied Parallel Programming course asks students to work with a specialized coding agent to generate complex GPU-based programs, while remaining responsible for testing, validating, and refining the code.

Finally, a set of proposals explores AI in creative and professional contexts. In Film and Media Studies, Deirdre Maitre uses a domain-specific AI system to analyze patterns in students’ own self-reported data, turning machine learning into a reflective tool rather than a content generator. In the School of Law’s Entrepreneurship Clinic, Jonathan Smith has students use generative AI as a drafting partner for legal documents, testing its usefulness and limits in real client-facing work.

With several required and high-enrollment courses such as College Writing included in the program, thousands of students each year will encounter assignments shaped through the program.

Faculty in the program will meet during week-long workshops this summer, with check-ins and ongoing collaboration scheduled throughout the academic year.

As courses and assignments are piloted, WashU faculty will be able to participate in opportunities to learn from the Corps’ pedagogical approaches and adapt them in their own teaching. The AI Curriculum Corps will host another cohort of faculty in Fall 2026, with the call for proposals opening this summer.