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Find similar grantsI-Corps: New image processing programs and data modeling algorithms for education environments is sponsored by National Science Foundation (NSF). This I-Corps project focuses on developing an AI solution using computer vision, deep learning, machine learning, and natural language processing to analyze student behavior and enhance learning performance.
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I-Corps: New image processing programs and data modeling algorithms for education environments | Pandemic PACT Tracker I-Corps: New image processing programs and data modeling algorithms for education environments Funded by National Science Foundation (NSF) Total publications: 2 publications Known Financial Commitments (USD) National Science Foundation (NSF) Lead Research Institution Research Priority Alignment Secondary impacts of disease, response & control measures The broader impact/commercial potential of this I-Corps project is the development of an AI (artificial intelligence) solution that is aimed at enhancing student active learning.
This technology platform is aimed at providing dynamic assessments of student performance throughout the academic year. The AI technology triggers timely interventions by providing early detection of struggling students as well as students with special talents.
Unlike traditional platforms, this solution uses a combination of factors such as students' behavior in the classroom, homework grades ,and regular test scores to evaluate risk levels and recommends generalized and personalized feedback plus identified routines for improving student learning performance. The platform also will communicate students' progress to students/parents/teachers regularly.
The proposed technology may enhance the learning experience by taking an approach that excludes the flaws of current system surfaced by the COVID-19 pandemic. This I-Corps project is based on the development of an AI (artificial intelligence) solution that uses advanced analytics to enhance students' learning performance.
The proposed technology uses a a combination of AI tools such as computer vision, deep learning, machine learning, and natural language processing to thoroughly analyze students' behavior inside and outside the classroom. It provides important prescriptive analytics and uses recommendation systems and collaborative filtering to provide dynamic feedback and identifies successful routines for improving the student learning performance.
This project is based on several behavior data science studies and the power of advanced analytics for generating data-driven insights in education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
2 Publications linked via Europe PMC View all publications at Europe PMC A ratiometric fluorescence sensor based on a bifunctional Cu-MOF nanozyme for dual-signal detection of glyphosate in actual samples. Zhang Y, Liu C, Ge H, Zheng T, Wang J, Wang Y.
Analytical methods : advancing methods and applications Effects of exercise on multiple health outcomes in children and adolescents with overweight or obesity: a meta-analysis of 176 randomized controlled trials and its implications for global obesity prevention. Men J, Wang P, Wang J, Zhu G, Yu Z, Wu S, Zhang Y, An W, Li Z, Ma R, Zhang R, Li S, Wang Y, Liu P. The international journal of behavioral nutrition and physical activity 10.
1186/s12966-026-01913-0
According to the current listing, eligibility includes: Universities and other eligible research institutions. Confirm the full requirements in the official notice before applying.
The current listing shows $50,000. Verify award ceilings, matching requirements, and allowable costs in the official notice.
I-Corps: New image processing programs and data modeling algorithms for education environments is funded by National Science Foundation (NSF). Verify program details on the funder's official page before applying.
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TCUP lists eight funding tracks and roughly $10.3M a year, but the October 14, 2026 deadline applies to only three of them — CHAI, Pre-TI, and TCUP Partnerships — and each carries a restriction that disqualifies most applicants. Here is the track-by-track math.
Read articleNSF 26-513 makes roughly $100 million available for up to 10 State and Regional AI Infrastructure Hubs at $4M to $12M each over five years. One award per state or multi-state region. One proposal per organization. And NSF is not buying you GPUs — it funds the coordination, the workforce and the faculty training, while the compute has to come from partners you have to already have.
Read articleAs of September 12, NSF had obligated $6.3 billion across 6,200 grants versus $8.1 billion and 8,600 last year. AHRQ has made 61 awards. Judge Allison Burroughs ordered the government to report by September 28 on whether IES will obligate $180 million before it expires. Here is what actually happens to the money on October 1 — and what it means for your FY2027 application.
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