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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.
Empire AI is New York State's answer to the compute-access problem, and this is the Cornell route into it. The consortium operates two machines: Alpha+, with 144 NVIDIA H100 GPUs, and Beta, which brings 288 B200 GPUs plus Grace CPU nodes online from December 2025. Allocations are denominated in Service Units, where one SU buys an hour of H100 time and B200 time costs two SU per hour - a pricing structure worth modelling carefully, since the newer hardware is not automatically the better value for every workload. The economics are the most consequential detail: the Cornell Provost underwrites the cost of allocations through November 2026, after which SUs carry a $0.50 charge. Applicants should therefore plan around that transition rather than assume indefinite free access. The current call covers the December 2025 to November 2026 allocation year and closes 17 September at midnight Eastern. Eligibility is narrow by design - faculty holding PI status at Cornell University, Cornell Tech or Weill Cornell Medicine - so this is not a national programme, but the same Empire AI resource is accessible through parallel calls at the other consortium institutions, and researchers at Columbia, NYU, RPI, SUNY and CUNY campuses should look for their own institution's route. The application is two-part: a Cornell-specific form plus an Empire AI-hosted form covering project details and compute requirements. Because the consortium is backed by state money and philanthropy rather than a commercial provider, there are no equity, credit-expiry or vendor-lock conditions attached.
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.
The Hydrocarbons and Geothermal Energy Office's University Training and Research program funds coal, oil and gas, and geothermal R&D at U.S. colleges and universities — but every proposal must include a non-academic partner and must build training modules that outlive the award. The LOI deadline is October 1, 2026, with full applications 15 days later. Here is what that compressed window means and why the workforce framing changes what a competitive proposal looks like.
Read articleDARPA's DIAL program already had AI rediscover the Kalman filter and wavelets from scratch. SPEED DIAL (DPA26TZ05-DV003) funds the unglamorous next step: turning algorithm discovery into a tool that plugs into MATLAB, Simulink, and COMSOL. Up to $750,000 base plus a $1.25 million option, Direct to Phase II only, with a three-accomplishment feasibility gate and a mandatory university partner. Here is the milestone schedule, the interpretability requirement, and who is actually eligible.
Read articleOn June 1, DARPA and NSF announced AI Forge — a jointly governed forum that will fund university-led research on three thrusts: AI interpretability, AI control, and adversarial robustness. The RFI on sam.gov closes June 22, 2026, at 5:00 PM ET. Project Ventures awards run roughly $750K to $3M with one-year durations and multiple awards expected annually. Administration runs through a nonprofit, intellectual property will be shared via open-source licensing, and CAISI at NIST is the third partner. Here is what the 15 priority research challenges look like and how U.S. universities should respond.
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