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Find similar grantsAI + Education Initiative - Generative AI for the Future of Learning is sponsored by Stanford Accelerator for Learning (in collaboration with the Stanford Institute for Human-Centered Artificial Intelligence). This initiative provides grants for innovative designs and/or research on critical issues and applications of generative AI in learning contexts.
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AI + Education • Stanford Accelerator for Learning Our work: Digital Learning As generative AI technology becomes increasingly widely used and technologically advanced, the Stanford Accelerator for Learning is responding rapidly to guide the use of AI in education to enhance learning rather than reproduce teaching methods that don’t work.
The November 2022 release of ChatGPT sparked a global re-examination of how we learn, teach, create, work, and connect. The Stanford Accelerator for Learning was ready for that reckoning.
Long before GenAI captured public attention, Stanford faculty and students—spanning education, computer science, neuroscience, psychology, business, economics, and design—had been exploring the intersection of big data, artificial intelligence, learning sciences, and human-centered design.
The Accelerator’s program in AI + Education supports educators and education leaders as they navigate a rapidly evolving landscape in a few ways: Activating new research and design.
The program has funded and supported over 30 interdisciplinary research projects on AI and education, leading to key insights in AI tutoring, teacher feedback and instructional coaching, AI literacy, medical education, and personalized learning for neurodiverse learners, to name a few. Engaging classroom educators.
The Accelerator works with educators and education leaders on AI literacy and professional learning experiences through programs including the AI Tinkery, CRAFT, CSET, and the GenAI Hub for Education. Convening teachers, students, researchers, and tech developers.
The annual AI + Education Summit, co-hosted with the Stanford Institute for Human-Centered Artificial Intelligence (HAI), brings together researchers, educators, and technologists to learn from each other and chart the future of learning. Shaping the field’s most pressing questions.
The program works across all seven Stanford schools to ask the big questions and influence the discourse on key questions like AI and cheating and learning through creation with AI. Link to AI challenges core assumptions in education AI challenges core assumptions in education We need to rethink student assessment, AI literacy, and technology’s usefulness, according to experts at the recent AI+Education Summit.
Generative AI is artificial intelligence that can generate novel content by using existing text, audio files, or images. Generative AI has now reached a tipping point where it can produce high quality output that can support many different kinds of tasks. For example, ChatGPT can write essays and code, DALL-E can create images and art, while other forms of generative AI can produce recipes, music, and videos.
These new forms of generative AI have the capacity to change how we think, create, teach, and also learn. The AI+Education program has collaborated with the Stanford Institute for Human-Centered Artificial Intelligence (HAI), to fund two rounds of early and exploratory stages of research on AI and education, including designs, prototypes, and pilot studies that may have the potential to scale or have broad impact in the future.
Link to Learning through Creation with Generative AI Learning through Creation with Generative AI The Stanford Accelerator for Learning and the Stanford Institute for Human-Centered Artificial Intelligence (HAI) invited research proposals advancing learning through creation with generative AI.
Link to Generative AI for the Future of Learning Generative AI for the Future of Learning In collaboration with the Stanford Institute for Human-Centered Artificial Intelligence, grants up to $100,000 for innovative designs and/or research on critical issues and applications of generative AI in learning contexts.
The Stanford Accelerator for Learning works with educators and education decision makers to facilitate the exchange of information and bridge the gap between research and practice. Educators help inform AI researchers and developers about PK-12’s challenges. They also suggest new areas of research and development to help address critical needs within the education system.
The Curricular Resources about AI for Teaching (CRAFT) project brings together researchers and teachers to co-create resources to teach AI literacies in high school. Housed in ANKO 020, the AI Tinkery is a collaborative space to play and tinker with the possibilities of generative AI in the classroom. It hosts regular events, open hours, and thematic working groups to envision how AI can support teaching and learning.
Professional Learning Extension (PLEX) Stanford Graduate School of Education Professional Learning Extension (PLEX) offers non-degree learning experiences for educators, leaders, and education professionals.
According to the current listing, eligibility includes: Researchers, educators, and education leaders (specifics may vary, but university affiliation is implied by the funder). Confirm the full requirements in the official notice before applying.
The current listing shows up to $100,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
AI + Education Initiative - Generative AI for the Future of Learning is funded by Stanford Accelerator for Learning (in collaboration with the Stanford Institute for Human-Centered Artificial Intelligence). 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.
Future CoRe (NSF 25-543) advertises $280 million and 400 to 600 awards across 11 programs, with a September 10, 2026 target date. The directorate behind it is running 43 percent below its own four-year average, $1 billion of NSF's budget is locked in a central account, and the two-proposal cap is enforced with no exceptions. Here is how to read the gap between what the solicitation promises and what the directorate is actually paying for.
Read articleDARPA's Defense Sciences Office pre-released two FY26 SBIR topics on July 1, 2026: FALCON (fusing efficient ML with large language models for interactive analysis of massive data) and Art of Novel Signals (temporal knowledge-graph forecasting from multilingual, multimodal data). Both opened July 22 and close August 19. Here is what each topic wants, how DARPA SBIR economics work, and the strategy to compete in a four-week window.
Read articleDARPA's FALCON SBIR topic (DPA26BZ04-DV016) is a Direct-to-Phase-II award worth $1.5 million to teams that can marry the statistical rigor of classical machine learning with the contextual reach of large language models. It opened July 22 and closes August 19, 2026. Here is why the no-Phase-I structure changes who can win, what the hallucination-mitigation requirement really demands, and how a small team should sequence a proposal in under four weeks.
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