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The CU System Sprint Grant is a small, fast-turnaround internal award supporting faculty who want to develop and implement AI-driven pedagogical strategies within a single course. At up to $20,000 with an explicit allowance for one course buyout, it is designed around the real obstacle facing most faculty experimenting with generative AI in teaching - not equipment or data, but protected time.
The single-course framing is a deliberate scoping decision and applicants should respect it: proposals aimed at curriculum-wide or program-level transformation are a poor fit, while a concrete plan to redesign one course around AI tools and measure the effect on student learning outcomes matches what the program is asking for.
Eligibility extends to tenured and tenure-track faculty as well as full-time and instructional series faculty across all CU campuses, which is broader than many internal research awards and deliberately includes teaching-focused appointments who are often closest to the pedagogical questions at issue. This is the second of three planned cycles, so faculty who miss the October 16, 2026 deadline should expect a third round.
For a grants database this entry is narrower in eligibility than a federal or foundation program - it is open only to CU faculty - but it is a useful example of the institutional micro-grant tier that has grown rapidly as universities respond to generative AI in the classroom, and comparable programs now exist at many university systems.
Faculty at other institutions who find this relevant should check whether their own provost or academic affairs office runs an equivalent scheme, since these internal AI teaching grants are frequently under-advertised and undersubscribed relative to external competitions.
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Or search similar grants →According to the current listing, eligibility includes: Eligibility is limited to tenured or tenure-track faculty and full-time and instructional series faculty at University of Colorado System campuses. Proposals should support innovative uses of AI in teaching and learning, specifically developing and implementing AI-driven pedagogical strategies to enhance student learning outcomes within a single course; program-wide or curriculum-level proposals are outside the intended scope. Awards are up to $20,000 and funds may cover project expenses including support for one course buyout. This is the second of three planned sprint grant cycles, so a third round is anticipated. Applications are due October 16, 2026. Details are available from the CU Office of Academic Affairs at cu.edu/oaa/cu-system-sprint-grant-ai-teaching-learning, and questions may be directed to AcademicAffairs@cu.edu. Confirm the full requirements in the official notice before applying.
The current listing shows grants provide up to $20,000 each. Funds may cover project expenses including support for one course buyout. The number of awards was not published. This is the second of three planned sprint grant cycles. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for University of Colorado System Sprint Grant: AI for Teaching and Learning Fall 2026 Cycle are due October 16, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
University of Colorado System Sprint Grant: AI for Teaching and Learning Fall 2026 Cycle is funded by University of Colorado System, Office of Academic Affairs, in partnership with the Office of the President. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The Oberndorf AI Medical Catalyst Grant funds University of Florida medical students to carry out research applying artificial intelligence to medical problems, with each award set at 5,000 US dollars. It sits within the Oberndorf AI Medical Scholarship Program, endowed by a donation from Lou and Rosemary Oberndorf and run jointly by the UF College of Medicine Office of Research and the Intelligent Clinical Care Center, known as IC3. The programme's design reflects a specific view of what limits clinical AI training: it does not require applicants to have prior AI experience, but it does require that each project have an AI mentor who is a current IC3 member or has membership pending and who brings extensive AI experience. In other words, the money follows the mentorship rather than the credential, which makes the award genuinely accessible to clinically oriented students who have the interest but not yet the technical background. Eligible project scope spans clinical decision-making, diagnostics, research and healthcare management, and past awardees have worked on large language models over electronic health record data, predictive analytics for kidney disease, and AI image segmentation in medical imaging. The most recently published cycle closed on 5 January 2026 with submission through the University of Florida InfoReady platform. No deadline is recorded here because the next cycle's date has not been published. For UF medical students, this is the designated on-ramp into AI research; for everyone else it is a useful model of how an institution can lower the barrier to clinical AI training.
The MIT Generative AI Impact Consortium (MGAIC) is an MIT-wide initiative bringing together industry partners and MIT faculty to advance generative AI research with high real-world impact. The consortium awards seed grants to MIT-led research teams across priority areas including: multimodal tactile sensing for robotics, real-time collaborative AI agents (e.g., jam_bots for live human-AI musical improvisation), understanding how LLM agents deviate from human choices and decision-making, foundation models for scientific discovery, generative AI for design and engineering, AI for healthcare and biology, and AI-augmented education. Each consortium funding cycle issues call for proposals from MIT faculty, with industry partner alignment guiding priority areas. Industry members include Analog Devices, Coca-Cola, OpenAI, Tata, Cisco, TWG Global, SK Telecom, McKinsey, Citi, and Verizon. Selected projects benefit from industry collaboration, data sharing, compute access through partner companies, and pathway to commercialization or real-world deployment. The consortium is hosted by MIT Schwarzman College of Computing in partnership with MIT Sloan and benefits from cross-MIT participation including CSAIL, Media Lab, and MIT-IBM Watson AI Lab.
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