Sean Corp, Associate Director of Communications

Rehearse a difficult clinical conversation as many times as you need. Receive useful feedback while still working through a problem. Learn how to challenge an AI-generated answer rather than simply accept it.

Artificial intelligence tools are creating these critical learning opportunities for students. A new call for proposals from the Center for Academic Innovation invites faculty to develop and test ways to make those learning experiences possible.

The AI Implementation Call for Proposals supports faculty experiments that use artificial intelligence to address meaningful teaching and learning challenges. Selected projects can receive up to $15,000 for related expenses, along with implementation support from the center, including instructional design, project management, and technical support.

“We want faculty to start with the learning experience, not the technology,” said Julien DePauw, director of the center’s EdTech Accelerator initiative. “What do you want students to be able to understand, practice, demonstrate, or apply? From personalizing learning and creating opportunities to practice, to providing meaningful feedback and enabling students to demonstrate what they can actually do.

These are opportunities for faculty to develop new ideas and take current experiments to a new phase that would otherwise be difficult to create, personalize, or offer at scale.

From AI capability to learning experience

The call focuses on what AI makes possible for students. That includes personalized learning. Faculty could test ways to adapt resources, prompts, or content to what individual students know and where they need additional practice.

Another is roleplay and simulation. AI can create repeatable environments in which students apply knowledge, make decisions, and practice complex skills without requiring an instructor to recreate the scenario for every student. It also allows students to access practice that best suits their learning needs.  

Faculty can also investigate new approaches to assessment and feedback. Projects might test ways to provide students with more timely feedback, support reflection, or create authentic assessments that demonstrate what students can do rather than what they can recall.

Another area is skill validation and career readiness. Projects could explore how AI can help students demonstrate their capabilities through authentic, competency-based assessments, simulations, and real-world scenarios. Rather than simply documenting what students have completed or memorized, these approaches can help students identify, practice, demonstrate, and communicate the skills they have developed.

As generative AI becomes part of students’ academic and professional lives, projects can focus on critical thinking and AI literacy itself. Faculty might design learning experiences in which students identify bias, investigate the limitations of AI-generated information, verify sources, or defend their decisions about whether an AI tool is appropriate for a particular task.

Projects that explore novel approaches beyond the use cases already described are also encouraged. Proposals can use U-M GPT tools or other AI platforms and products. Projects involving new third-party tools must complete the university’s review process for data protection, security, and privacy.

Support for turning an idea into a pilot

The funding is intended to help faculty move an idea from concept to implementation and evaluation. Approved project funding could support expenses such as software licenses, API access, AI tokens, student wages, computing resources, user testing, prototype development, and specialized technical expertise.

But the aim is not simply to build new AI tools. It is to give faculty the resources to test where AI can meaningfully contribute to learning.  

“Faculty are expert teachers and communicators who can help us move beyond asking what tasks AI can do and instead ask what, specifically, AI can make possible for learning,” DePauw said. “The goal is not simply to add AI to a learning experience, but to identify the capabilities that address a particular learning need. By testing those capabilities in real courses against specific learning goals, we can better understand where AI adds meaningful value to the student experience—and where it may not.”

The center is also eager to work with faculty on projects that could scale beyond individual courses and colleges, with the potential to impact the broader U-M student community and even global learners in courses available on Michigan Online.

Faculty can begin that work on one of two timelines, depending on how developed their project idea is.

Those with an idea already taking shape can submit by the priority deadline of Nov. 06, 2026. Decisions will be announced Nov. 20, with projects beginning in early December and a full term available for development and piloting.

The final deadline is Jan. 15, 2027, for faculty who need more time to develop an idea, identify collaborators, secure department support, or complete the review of a new AI tool. Decisions will be announced Jan. 29, with projects beginning in February.

Both rounds use the same evaluation criteria and funding cap. Applying earlier provides more development time, not an advantage in the selection process.

Learn more about the submission criteria and timeline on the center’s AI Call for Proposal page.