AI Instructional Support Team: A Student–Faculty Partnership Approach to Advancing AI Integration into Teaching and Learning

Graduate students were hired as “AI Instructional Assistants” to partner with “AI Faculty Fellows” who had been selected by college leadership to advance responsible and ethical adoption of AI throughout a large, R1 university. Each student supported teams of two faculty in two different colleges. 

Ongoing professional development for IAs was responsive to faculty needs and included data gathering techniques (surveys and focus groups), bot development with Anthropic’s Claude, and project specification. Students and faculty were encouraged to approach the work using a partnership model as described by Cook-Sather, Bovill and Felton (2014) to best utilize the perspectives of each.

The Center for Advancing Teaching and Learning Through Research employs graduate students as AI Instructional Assistants (IAs) to support AI Faculty Fellows in advancing the integration of AI in teaching and learning. Each IA works with faculty from two of the university’s ten colleges. The work follows a partnership model described by Cook-Sather, Bovill and Felton (2014), as illustrated in the AI Instructional Assistant program components graphic, which can be found in the Google Drive below.

Graduate students receive initial training on pedagogical uses of AI and complete a landscape analysis of how AI is used in teaching at peer institutions within disciplines related to their assigned colleges. This supports IAs in working across unfamiliar disciplinary contexts and provides faculty with concrete reference points for instructional decision-making.

CATLR staff meet weekly with IAs to review projects, share progress, and provide feedback, alongside ongoing professional development responsive to faculty needs. The program is currently in its second year. In the first year, faculty needs focused on information gathering methods (surveys and focus groups) and communication strategies (workshops, presentations, and websites). In the second year, faculty needs shifted toward more technical support, leading to professional development in project specification, building complex projects in Anthropic Claude (the institution-provided LLM), and developing more advanced websites and digital resources.

Beyond faculty support, IAs identified ways students could use AI productively for learning. They developed a series of “By Students, For Students” written guides and short videos, and two IAs partnered with CATLR staff to design and facilitate a faculty workshop on teaching students to use AI as a study partner.

The AI Instructional Assistants program was evaluated as part of the broader AI in Teaching and Learning Across the Network initiative, which also included the AI Faculty Fellows program. Impact was measured using post-program surveys, participant reflections, the number of activities implemented, and participation counts in related events and initiatives.

The IA program itself was further evaluated through surveys of participating faculty and students, with findings used to strengthen the student–faculty partnership approach and refine role alignment between Faculty Fellows and IAs. To date, two cohorts of graduate students have been hired and supervised. Given the rapidly evolving nature of AI in teaching and learning, the program design is expected to remain dynamic over time.

Implementation Complexity: This practice involves significant staff time to hire and supervise graduate students over time. This supervision includes a weekly meeting of all IAs, tracking their work, and mentoring them individually when necessary.

• Student–faculty partnership model (Cook-Sather, Bovill and Felton, 2014)
• Graduate student hiring and supervision structures
• Weekly coordination and mentoring by CATLR staff
• Responsive professional development for IAs
• Institutional access to Anthropic Claude
• Strong central support from the Center for Advancing Teaching and Learning Through Research