North Carolina State University

NSF Awards · FY 2025 · 2025-10

With support from the Research Experiences for Undergraduates (REU) program, this project aims to create dynamic research experiences for undergraduate students in artificial intelligence (AI) with applications in computer science education. Many students from institutions with limited research infrastructure lack access to research opportunities in AI. This REU site will provide hands-on research opportunities in educational technologies and participants will develop AI-based educational resources for computer science education settings. The project will promote high-quality undergraduate research through a structured mentoring program and foster connections between research universities and other institutions. The specific aims of the project are to introduce participants to foundational principles of AI and machine learning, foster hands-on research experiences through collaborative projects, develop AI-based educational resources, and enable participants to lead educational experiences using their creations. The REU site will be structured as a 10-week summer experience where participant pairs will be matched with research mentors in active laboratories at North Carolina State University. Research projects will focus on natural language processing, educational games, multimodal learning analytics, and interactive narrative technologies, with applications in K-12 and higher education settings. As part of these projects, students will participate in a wide range of research activities, including usability testing, user studies, computational modeling, as well as qualitative and quantitative data analysis. Participants will engage in the full research cycle from ideation to evaluation and will present their findings at the university's research symposium, with potential for publication in computer science education conferences. The structured mentoring model includes graduate student near-peer mentors, professional research mentors, and weekly workshops covering research methods, communication, and technical skills. This project is funded by the EDU Core Research: Building Capacity in STEM Education Research (ECR: BCSER), which supports projects that build investigators' capacity to carry out high-quality STEM education research that will enhance the nation's STEM education enterprise. 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.

NSF Awards · FY 2025 · 2025-10

There are more than 50,000 islands in the world, accounting for 17% of the total land area and inhabited by 10% of the global population. The US accounts for 18,617 islands, where the cost of electricity such as in Alaskan and Pacific islands can be 4-8 times higher than the average in the US. The same is true for remote coastal communities, such as 200 miles of Outer Banks of North Carolina, 120 miles of Florida Keys, and many islands in the Great Lakes. For power utilities, these communities rely on imported fossil fuels or miles of umbilical cables, which are vulnerable to earthquakes, wildfires, hurricanes and storms. While the electricity supply is one of the challenges limiting socio-economic development of remote island and coastal communities, vast energy resources are available from ocean waves along the 95,471 miles of US coastline. The power density of ocean wave energy is over 10 times that of solar power and 5 times as much as wind power. Attempts to harvest this resource date back to 1799, when the first patent was issued. To date, about 250 concepts of wave energy converters (WECs) have been proposed, but none of these have achieved commercial success. There is not even a widely-accepted criterion by which to judge which WEC concept is most favorable. The objective of this project is to drive and achieve research convergence of ocean wave energy conversion for empowering remote coastal communities through transdisciplinary research across engineering, economics, environmental, and sociological dimensions. The team expects to achieve convergence for powering remote communities within 4-5 years. In the longer term, wave energy can directly benefit a large proportion of the U.S. population without long-distance transmission, since over 53% of the U.S. population is concentrated within 50 miles of the shoreline. The project will provide significant potential to improve the economic development of under-served coastal communities by identifying a practical route to renewable electricity, thereby increasing their resilience to natural disasters, and empowering the local economy. It will also substantially benefit education from K-12 to graduate students in four universities with an emphasis on professional skills development. This project will drive convergence of ocean wave energy research through community-engaged decision making, 3D techno-economic socio-environmental assessment, and transdisciplinary co-design methodology. The goal will be achieved in two phases. Phase I will develop the WEC convergence roadmap, screen and down-select 2-3 lead WEC design concepts. This will be achieved by creating 3D assessment metrics to systematically evaluate technological feasibility, economic viability, and socioenvironmental acceptability in the early foundational concept and design stage. Phase II will investigate the leading design concepts through transdisciplinary co-design and optimization, and validate the convergence through community engagement and ocean tests. Inspired by the drug discovery process, the project will use a market-pull convergence procedure based on the needs of remote coastal communities to screen various WEC concepts from the beginning. This is in contrast to the prevailing approaches in wave energy research and development. The project includes a multidisciplinary team consisting of experts in engineering, environment, sustainability, social science and an external advisory board with community end users and OEM developers to implement a transdisciplinary, community-engaged approach to this research challenge. 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.

NSF Awards · FY 2025 · 2025-10

Opioid use disorder (OUD) and addiction affects approximately 3.7% of U.S. adults (9.37 million) and caused more than 70,000 deaths from fentanyl overdoses in 2023. However, we have a limited understanding of where, when, and how opioids modulate the diverse behavioral outputs of the brain. This is partly due to the limited technology available for in vivo opioid monitoring in the brain. This project aims to develop new technology to monitor fentanyl in the brain. The developed technology has a significant impact in several settings. It offers urgently needed technologies to understand with high spatiotemporal resolution how opioids modulate diverse behavioral outputs of the brain. Moreover, the underlying bioelectronic design principles and knowledge generated will be applicable to other fields, including biosensors for therapeutic drug monitoring, immune response tracking, and chronic disease management. The project will involve high school and undergraduate students. Students will receive training in experimental techniques, data analysis, and scientific writing. New course modules leveraging the results of the work will be incorporated into existing undergraduate and graduate courses at North Carolina State University and the University of Connecticut. The goal of the project is to develop and characterize a wireless bioelectronic system for high-performance fentanyl monitoring in the brain of freely moving small animal models. To achieve this goal, we will: 1) isolate, characterize, and engineer aptamers targeting fentanyl, a primary opioid associated with OUD, 2) develop an implantable nanoporous electrochemical aptamer-based (nanoEAB) fentanyl sensor, and study the structure–property relationship of a new surface coating to improve its in vivo longevity, and 3) establish and validate a wireless bioelectronic system for fentanyl monitoring in the brain of freely moving animals. The project will significantly advance the design and development of wireless bioelectronic systems for high-performance fentanyl monitoring in the brain. Additionally, the developed fentanyl sensors could serve as a technology platform for developing wearable emergency response systems for real-time opioid monitoring and closed-loop delivery of therapeutic drugs such as naloxone. Finally, due to the generalizability of the aptamer selection and nanoEAB platform, this technology will serve as a template for designing sensors for monitoring other molecules of biomedical interest by simply replacing the aptamers functionalized on the sensor surface. 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.

NSF Awards · FY 2025 · 2025-10

This Research Experiences for Undergraduates (REU) Site, Nanoscale Detectives, offers undergraduate students an immersive research experience investigating the fundamental structure, dynamics, and properties of hybrid perovskite materials — a class of materials with transformative potential in next-generation semiconductor technologies, including solar cells, photodetectors, and advanced computing. By engaging students from various academic disciplines in cutting-edge, hands-on research across the three academic institutions in making up North Carolina’s Research Triangle Area (NC State University, UNC Chapel Hill, and Duke), the program addresses the critical national need to cultivate a highly skilled STEM workforce equipped to tackle complex challenges in materials science, semiconductors and energy security. The REU Site promotes the progress of science through discovery-focused learning, while advancing national prosperity by preparing students for graduate education and careers in high-demand STEM fields. The program also recruits and trains students from community colleges as well as first-generation college students, in support of the nation’s commitment to expanding the STEM workforce in semiconductors and other strategic areas. The Nanoscale Detectives REU Site will host cohorts of twelve undergraduate students each summer for a ten-week research program at NC State University, UNC Chapel Hill, and Duke University. Participants will work closely with faculty and graduate student mentors on interdisciplinary projects that apply advanced synthesis, spectroscopy, microscopy, and computational modeling to elucidate the nanoscale structure and dynamic behavior of hybrid perovskite systems. The program includes a structured professional development curriculum, mentor training workshops, and outreach activities designed to enhance student skills in research communication, ethics, and career planning. Research outcomes will contribute new insights into the stability and performance of perovskite materials, with the potential to inform the design of more efficient and durable devices for renewable energy technologies. Program assessment will combine formative and summative evaluations, with anonymized data analyzed to inform continuous improvement and shared broadly with the undergraduate research community. Through these activities, the REU Site will advance fundamental understanding of hybrid perovskites and prepare the next generation of scientists and engineers to lead innovation in energy and materials science. This Site is supported in part by funds provided to the National Science Foundation by the Semiconductor Research Corporation. 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.

NSF Awards · FY 2025 · 2025-10

Planning, deploying, and engineering the networks that make up the Internet infrastructure involves complex problems referred to generically as “network design” problems. Effective and efficient solutions to network design problems are crucial to the operation and economics of the Internet and its ability to support critical and reliable communication services. This is especially true for optical networks that form the foundation of the global network infrastructure. This project develops new capabilities for the design and operation of optical networks that underlie ubiquitous Internet connectivity. Its framework makes it possible to leverage existing high performance computing resources to tackle Internet-scale network design problems. Therefore, the project produces research results that will enable a wide range of 21st century science, education, and commercial applications through the design of networks that are better optimized for user and application requirements and are less expensive to build, engineer, and operate. Earlier algorithmic methods for tackling optical network design problems have two crucial limitations. The first relates to spectrum symmetry, i.e., the fact that wavelengths and spectrum slots are interchangeable. Symmetry is particularly challenging for conventional Integer Linear Programming formulations as they encompass an exponential number of symmetric yet equivalent solutions. The second challenge is that network design algorithms have not been developed with parallelism in mind, hence they cannot take advantage of abundant and readily available High Performance Computing or cloud resources to speed up the execution time for large problem instances via multi-threading. This project’s symmetry-free model for spectrum assignment in general topology networks enables, for the first time, the design of a new breed of optimal algorithms that altogether sidestep spectrum symmetry, i.e., eliminate symmetric solutions from consideration. Moreover, parallelism is inherent in this model as the new, smaller solution space may be naturally decomposed into independent subsets. Accordingly, new algorithms are developed to readily admit multithreaded implementations and may be tailored to the computing environment at hand. This work makes contributions that lead to a paradigm shift in how we think about network design problems and develops solutions to tackle them in an efficient and scalable manner. Both computational and architectural approaches that are employed are applicable to a wide range of problems. An important outcome of this research is to lower the barrier to fully exploring the solution space and in implementing and deploying innovative designs. This work will enable large-scale networking to flourish, and indeed, it is required for its sustainability. 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.

NSF Awards · FY 2025 · 2025-10

In K-12 computer science (CS) education, collaboration is a key practice for all grade levels in CS standards, but there are still significant gaps in understanding how students can be supported in collaborating. In addition to being a central workforce skill, collaboration also supports students' sense of agency, sustained engagement, and conceptual understanding of CS. This project advances the field's understanding of student collaboration in CS education, focusing on upper elementary students. The project explores how different models of collaborative programming can impact quality of collaboration, student engagement, learning outcomes, and attitudes toward CS. By investigating these models, the project is determining how varying the types of CS activities, number of computers, student roles, and pairing strategies can influence students' collaborative practices; and through these their conceptual understanding of CS, their interest in the subject, and their teachers' confidence in leading CS instruction. The findings will help educators better support students' sustained engagement in programming activities, conscious control over their learning, and understanding of CS concepts. Dissemination of these findings is through both reports of research results and through models of instruction that can be employed with different CS curricula. This study of fourth and fifth grade classroom work in computer science compares and examines student learning across different conditions of dyad collaboration on computers. One condition has two students work at one computer; another condition has them work in sync on two computers in the same online space; and a third condition has them work in sync on two computers while guided to play roles of proposer and reviewer. The idea is to mimic problem solving through collaboration as it is important in CS industry, while also leveraging the collaboration as learning support. Prior research has found that assigning roles helps students be more productive, and this study examines this in more detail both in terms of the outcomes, quantitatively documented, and qualitatively in terms of the processes of learning. North Carolina State University and SRI partner in this work, which is conducted in elementary schools in North Carolina and California. A pilot study of 24 students and focus groups refines instruments and procedures. A larger study is conducted with 300 4th and 5th graders. This project is co-funded by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This project also is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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.

NSF Awards · FY 2025 · 2025-10

The Textiles of Tomorrow REU Site will support eight undergraduate students each summer for 10 weeks of research and hands-on experience within the Textile Engineering, Chemistry and Science (TECS) department at NC State University. Research has shown that engaging students early in enriching research experiences is a highly effective approach for attracting, cultivating, and retaining individuals in STEM careers, as well as bolstering their motivation to pursue a graduate degree. Research projects will focus on innovative new materials, including the development of biodegradable and nano fibers for medical and other applications, such as tissue engineering, renewable dyes and auxiliaries, green composites, low-energy coloration methods, and smart textiles (i.e., electronically embedded fibers and yarns) for health monitoring. The TECS department is an interdisciplinary department where faculty range from the physical sciences (i.e., organic, inorganic, and polymer chemistry and fiber and polymer sciences) to various engineering disciplines (i.e., industrial, mechanical, aerospace, electrical, chemical, bio-medical/physiology, and materials science), giving students a unique experience in solving grand challenges that society faces today. Participants will learn about a remarkable, large-scale textile industry that encompasses unique opportunities at traditional textile and apparel companies, such as Nike, Under Armour, North Face, and Patagonia, as well as non-traditional textile companies like Apple, Meta, Tesla, Google, and Intel. To tackle the prevalent grand challenges confronting our world today, we face not only the need for innovative solutions but also the need to cultivate a young pool of the next generation to push the boundaries of research and accelerate innovation in STEM fields. Thus, the new external REU site will be housed at NC State University in the College of Engineering and the Wilson College of Textiles. The site will be committed to establishing a transformative research experience for undergraduate students in the fields of textile engineering, chemistry and science (TECS). The TECS department is globally recognized as the preeminent hub for textile education, research, and innovation. The TECS department has a strong core of faculty engaged in research across a broad range of disciplines within the physical sciences and traditional engineering. The proposed new REU site will complement our well-established TECS Internal REU program, which has earned high accolades from the NC State Chancellor and is the largest and most comprehensive program of its kind at the university. Through the new REU site, these external students will interact with our internal TECS students, bringing a broader perspective to each group. Having internal students will allow external students to be more connected to the university and the city of Raleigh. Like the well-established TECS REU program, the proposed new REU site will offer enriching hands-on research experiences and professional development workshops, fostering engagement between faculty and graduate and undergraduate students. Faculty mentors and other research staff will guide students through the entire research process — from literature review and experimental design to analysis and communication of findings. Programming will include rotating additional faculty office hours, peer-led technical sessions, and professional development workshops, which will cover both scientific topics like reading and summarizing literature, analyzing findings, learning about microscopy and other equipment, and presenting and communicating technical topics, and professional topics like tours of extensive facilities, visits to companies, resume building, LinkedIn and networking, interview skills, and going to graduate school. Student participants will have multiple opportunities to present their research in public, including oral presentations in group meetings, peer presentations, and a poster presentation at the campus-wide undergraduate research symposium, which will take place at the end of the program. It will recruit participants through partnerships with a variety of institutional types, including community colleges, teaching-focused colleges, and schools with limited research capacity. All engagement and recruitment efforts will be open to all eligible students, in accordance with the NSF's guidelines. 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.

NSF Awards · FY 2025 · 2025-10

Electric vehicles (EVs) are envisioned to be an integral component for the US power distribution system in the coming years, raising questions on how the control logic, scheduling, and pricing of EV charging may adversely impact the dynamical properties and stability of the distribution grid. If the number of EVs keep increasing, and if EV owners keep following the same control and pricing mechanisms as they do now, that may encourage a large fraction of drivers to charge their cars at certain specific times of the day thereby causing overloading problems in the grid. Grid operators, therefore, must find practical ways for incentivizing EV customers to settle for a slightly lesser charging demand than what they want for the same charging duration, and thereby control charging patterns across neighborhoods and cities so that instability issues in the local distribution grid can be prevented. The objective of this EAGER project is to take a step forward towards addressing this practical challenge. Both model-driven and data-driven optimization and control algorithms will be developed to translate EV charging information submitted by drivers to suitable routing and scheduling routines that come with guaranteed small-signal stability and voltage stability of the grid. The intellectual merit of this project lies primarily on evaluating massive-scale EV integration and developing a new understanding of power electronics-based control mechanism based on the principles and applications of dynamics and control systems theory to help prevent instability in electric power grid. The broader impacts include bridging a long-standing gap between control theory and vehicular power electronics, integrating research results with power system courses, organizing workshops and conference tutorials, and collaboration with industry. This project will address two main tasks. The first task will be to understand the fundamental relationship between charging currents drawn through DC fast chargers and the stability margins of the grid where these chargers are installed. Optimization and control algorithms will be developed to maximize these margins in return of monetary incentivization offered to EV owners. In some cases, this relationship may not follow entirely from the physical knowledge of the charging circuits, in which case machine learning based methods will be used. The second task will address scenarios where some EV owners may bias the optimization problem by submitting inaccurate information about their charging demands to maximize their individual incentives. Strategies will be developed to eliminate such biases using ideas from game theory, optimal control, and adaptive dynamic programming. The study will promote many new directions of theoretical and experimental research for tomorrow’s energy networks and their integration with transportation networks. 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.

NSF Awards · FY 2025 · 2025-10

This project aims to serve the national interest by developing STEM courses that will prepare students to apply knowledge from mathematics courses to other disciplines. Undergraduate students are increasingly asked to make connections and articulate problems from disciplines outside of mathematics in quantitative terms. College faculty also need to understand and meet the changing educational needs of students, and most institutions have limited resources to address this critical challenge. The National Consortium for Synergistic Undergraduate Mathematics via Multi-institutional Interdisciplinary Teaching Partnerships (SUMMIT-P) has been working since 2016 to address these issues by forming and strengthening faculty partnerships across disciplines and institutions. These partnerships have reduced the separation between disciplines, resulting in undergraduate courses that make explicit connections between mathematics and numerous other disciplines. This Level 2 IUSE Institutional and Community Transformation project, led by West Virginia University, Virginia Commonwealth University, and Western Michigan University, includes approximately 40 new institutions that will adapt the SUMMIT-P model to form local faculty teams. The SUMMIT-P team plans to study the experiences of students in these revised courses while also collaborating with national professional societies to ensure the long-term sustainability of the consortium’s work, significantly expanding the scope and positive impact of SUMMIT-P to thousands of additional college students. The goal of this project is to assist institutions with the adaptation of a known model for developing and implementing cross-disciplinary STEM courses. The project will also work to broadly disseminate the SUMMIT-P model to institutions beyond the intended 40 project participants. The project will support the development of sustainable collaborations that aim to minimize traditional disciplinary silos. Additionally, the proposed work will advance understanding of the student experience in these interdisciplinary courses, using previous student outcomes in SUMMIT-P courses combined with new data focused on long-term student impact. The project’s research plan is designed to advance understanding of how choices made by institution-based teams affect positive impact of the change process, as well as the institutional qualities and resources that are critical to lasting change. Additionally, the research team plans to study the effectiveness of faculty partnerships formed using SUMMIT-P change processes in creating significant and lasting curricular change that results in positive long-term student impact. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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.

NSF Awards · FY 2025 · 2025-10

The ElementaryAI project forms a research-practice partnership (RPP) between North Carolina State University and Montgomery County (North Carolina) Schools to address a critical need for improving student outcomes in English Language Arts and mathematics through the integration of artificial intelligence (AI) education. This project leverages elementary school children's excitement around AI to enhance learning in ELA and math while preparing students to take advantage of middle and high school opportunities to learn about drones and AI. The project is also designed to significantly enhance elementary school teachers' AI literacy and readiness to integrate AI in their classrooms. The project employs a highly collaborative and iterative process working with teachers to determine areas for learning improvement, and design new AI-integrated curricula to address those areas. The project will generate new knowledge on effective models for teacher professional development, curriculum design, and AI literacy assessment in elementary education. Montgomery County Schools will partner with NC State to integrate AI throughout the schools can improve teacher and student engagement and motivation, while engaging the broader community with more access to learning and technology to enhance readiness for AI and STEM careers. Key products of the ElementaryAI project are the development of a suite of AI-integrated elementary curricula, as well as adapted AI literacy scales for elementary students and teachers, both aligned using a 4-step AI thinking process related to computational thinking principles and to the big 5 ideas in the AI4K12 guidelines. The project employs a design-based implementation research (DBIR) approach, positioning teachers as co-designers and instructional leaders in adapting and enacting AI-integrated curricula aligned to school and district needs. The project will develop a teacher professional and leadership development model relying on the elementary school structure of STEM, art, media, and physical education specialist teachers, and investigate whether it is effective for disseminating AI-integrated curricula throughout 6 elementary schools in the county. The project will investigate and develop knowledge about: 1) best practices for improving teacher self-efficacy and willingness to teach AI, 2) barriers to AI adoption in elementary schools, and 3) effectiveness of AI integration in improving student AI literacy and performance in ELA and math. The joint RPP identification of barriers and opportunities for integrating AI in all elementary schools in one district can help similar schools and districts adopt these strategies more successfully. Since the NCSU project team members work closely with the NC Department of Public Instruction and State Board of Education, the project may potentially have a significant impact on statewide K-5 educational policies and programs in North Carolina. 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.

NSF Awards · FY 2025 · 2025-10

Barrier island breaches have occurred during many tropical storms, constituting a major mechanism for tidal inlet formation, dune and beach erosion and development. Thus, they represent a major challenge to coastal management. The current understanding of the fate, physical processes, and impacts surrounding new and evolving breaches is limited due to the lack of comprehensive longitudinal studies capturing the breaching event and post-breaching evolution on monthly and annual time scales in a holistic and transdisciplinary manner. To address the current gaps in knowledge and data, this EArly-concept Grant for Experimental Research (EAGER) study investigates the development of two barrier island breaches from their original formation over multiple seasons and years, and their potential impacts on coastal management and infrastructure systems. The "high-risk high-outcome" study is expected to reveal new insights into the roles of hydrodynamics, land coverage, and geomechanical sediment properties on barrier island breach evolution, as well as into the impacts of these newly formed inlets on coastal infrastructure systems. It looks to unravel the importance of barrier island breach data collection for informed coastal management, planning, engineering design, and decision-making in coastal regions affected by storms. The data are expected to become a benchmark data set that will serve the wider coastal research community for calibration and validation of numerical and physical models and the development of new concepts, relationships, and theories regarding the geomorphological evolution of storm-induced barrier island breaches, local hydrodynamics, surrounding sediment and land-use conditions, coastal infrastructure, and the built environment. Midnight Pass breach in Venice, Florida, and Milton Pass breach in Englewood, Florida, opened during the 2024 sequence of Hurricanes Helene and Milton and are located in the same geological and meteorological region. The two inlets will be investigated with focus on post-breach geomorphodynamics driven by small-scale variability in hydrodynamics, sediment properties, geomorphology, vegetation, and anthropogenic influences from engineering actions and land use. The study seeks to leverage and extend the interdisciplinary field data collections following the storms and in 2025, complementing the effort with analyses and initial application to existing numerical models. The project intends to also test and assess newly emerging instrumentation and cross-disciplinary data collection strategies for storm-related geomorphodynamics and infrastructure system performance research. The study seeks to build on and strengthens an interdisciplinary network of natural hazards sciences and engineering researchers. 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.

NSF Awards · FY 2025 · 2025-10

Cold atom technologies lie at the heart of the emerging quantum revolution, enabling applications in sensing, navigation, timing, and fundamental physics. However, the conventional setups used to trap and cool atoms, known as magneto-optical traps (MOTs), are bulky, power-hungry, and require complex optical alignments, limiting their use to laboratory environments. The proposed research aims to miniaturize MOTs by replacing traditional free-space optics with chip-scale nanophotonic components. Using advanced metasurfaces and planar diffraction gratings, the project will realize a compact MOT that uses only a single input laser beam to trap millions of atoms. The resulting platform will dramatically reduce size, weight, and power consumption, paving the way towards portable cold-atom systems. The miniaturized MOT developed in this project will support new capabilities in quantum sensing, including portable electromagnetic field sensors based on highly sensitive Rydberg atoms. The compact and scalable nature of the platform opens opportunities for integration into photonic and electronic systems, pushing forward the development of quantum technologies at the chip scale. Educational efforts will include an undergraduate summer research program targeting community college transfer students, curriculum development in quantum photonics, and public outreach through school programs and museum exhibitions. This integrated research and education effort will help grow a diverse, quantum-ready engineering workforce and support U.S. leadership in the quantum and photonic technologies of the future. This research will demonstrate a compact nanophotonic-atomic platform for laser cooling and trapping of neutral atoms using multifunctional metasurfaces and high-efficiency 2D diffraction gratings. The system eliminates bulky optics by integrating a metasurface that performs beam expansion, polarization control, and flat-top shaping in a single planar element. A co-designed 2D grating chip then diffracts the shaped beam into the multiple paths required for magneto-optical trapping. Together, these components form a chip-scale MOT architecture capable of trapping ~10⁶ ⁸⁷Rb atoms at Doppler-limited temperatures, with future compatibility for sub-Doppler cooling. Metasurface development will follow a two-stage approach. Stage I will validate a proof-of-concept metasurface that replaces multiple bulk components by demonstrating all necessary beam transformations for MOT operation. Stage II will optimize the design for tighter integration. To complement the metasurface, a planar dielectric diffraction grating with a metallic back reflector will be designed to achieve high diffraction efficiency, correct circular polarization handedness, and balanced power distribution across multiple output beams. Its chip-scale form supports seamless integration and enables self-aligned MOT configurations that reduce system complexity. 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.

NSF Awards · FY 2025 · 2025-10

This Level 2 Engaged Student Learning IUSE (Improving Undergraduate STEM Education) project aims to serve the national interest by improving STEM students' engagement in science through the linkage of academic experiences with real-world problems that students will investigate in their mathematics, social science, and environmental classes. Participatory science, also known as citizen science, engages the public in the scientific process by having individuals assist with data collection, data interpretation, and research question formation. This type of science has demonstrated its ability to advance scientific knowledge while also connecting the public to various biological and community issues and concerns. The significance of this when considering the university campus communities of this collaborative project is that participatory science activities can also increase undergraduate student engagement in STEM classes. Recent research also suggests that these efforts may have broader impacts on students' overall educational experiences in college, which is an additional goal. This project will scaffold activities that move students from simply learning basic scientific concepts to applying best scientific practices, and then to evaluating and using qualitative and quantitative results to inform student recommendations for resolving a local problem they are investigating. As these community-based projects are incorporated into course curricula, the impact will be seen in the improvement of student performance in STEM classes and an increase in the number of students interested in STEM careers. This is often the result of helping students see the link between academic experiences and real world problems and how the scientific process can be used to study these issues. This project's overall goal is to enhance student learning outcomes in mathematics and data analytics through participatory science experiences. The Undergraduate Participatory Science Initiative (UPSI) in Data Analytics and Interdisciplinary StudY (DAISY) will consist of a scaffolded sequence of learning modules and research experiences grounded in participatory science, that will be available to students in general education mathematics courses and select courses in the social and environmental sciences. By integrating participatory science in the classroom, the project's scope of work will: 1) create an alternative research curriculum across disciplines; 2) train faculty across disciplines in utilizing participatory science in classroom; 3) provide students with engaging, community-based research training and leadership skills; and 4) improve environmental and student outcomes on campus and aid local communities through scientific research. The project will also involve a rigorous assessment of these experiences regarding enhancement of course-specific learning outcomes, as well as the extent to which they positively impact students' interest, self-efficacy, and connection to mathematics, science, and students' disciplinary major. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.

NSF Awards · FY 2025 · 2025-10

This project aims to enhance the NSF Aerial Experimentation and Research Platform on Advanced Wireless (AERPAW) at North Carolina State University. AERPAW is one of the projects under the NSF Platforms for Advanced Wireless Research (PAWR) program, supporting wireless experiments with autonomous unmanned aerial vehicles (UAVs). The proposed activities will: 1) develop and release new sample experiments with UAVs and software defined radios (SDRs); 2) release new wireless and UAV datasets; 3) enhance platform user support and add new portal features; and 4) conduct outreach activities to bring new users to the platform. The proposed work will release new sample experiments, post-processing scripts, and datasets, to support research in technologies such as O-RAN, spectrum sharing, trajectory optimization, wireless localization, and artificial intelligence (AI) aided spectrum sharing. The project will create publicly accessible short video tutorials integrated into the user manual, will improve AERPAW user portal, and will continue providing operational support, such as weekly office hours, to AERPAW users. Moreover, the AERPAW web portal and digital twin (DT) will be upgraded by incorporating key features requested by users, such as improved user key management and automated feedback mechanisms based on DT system activity. The broader impacts of the project include continued support for experimentation needs of the CISE research community in the area of advanced wireless systems and autonomous UAVs. The datasets to be released will serve as invaluable resources for developing and testing new AI tools based on real world UAV measurements that are otherwise difficult to capture. Outreach activities will bring new users to the platform and accelerate the transition of fundamental research concepts to real world implementation. The student competition to be hosted will train new users and bring fundamental research ideas to practice. AERPAW website, where project updates will be posted, can be accessed at https://aerpaw.org/. AERPAW User Manual includes instructions on how to use AERPAW and lists AERPAW sample experiments, available at https://sites.google.com/ncsu.edu/aerpaw-user-manual/. AERPAW Experiment Web Portal is available at https://user-web-portal.aerpaw.ncsu.edu/. AERPAW Datasets and software scripts are released through https://aerpaw.org/experiments/datasets/. These pages and repositories will be kept available at least five years following the end of the project term. 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.

NSF Awards · FY 2025 · 2025-10

Non-technical Description: The NeuroTronics project will create new materials that can seamlessly connect with the human nervous system, paving the way for advancements in bioelectronics. These materials, known as organic mixed ionic-electronic conductors (OMIECs), will be designed to efficiently conduct both electricity and ions and are crucial for developing improved brain-computer interfaces, therapies for neurological conditions, and more energy-efficient computing inspired by the human brain. This research could lead to breakthroughs in healthcare, human-AI interaction, computing, and robotics. The project will combine advanced computer modeling, machine learning, as well as automated and autonomous experimentation to create materials that are electronically adjustable, safe for use in the body, durable, and manufacturable at scale. A key focus will be training a new generation of scientists and engineers in AI-driven materials design through workshops and public outreach events like science museum demonstrations. By providing both fundamental knowledge and practical tools for material design, this project will overcome a major hurdle in creating reliable, mass-producible materials needed for real-world neuromorphic technologies that could eventually gain medical approval. Technical Description: This research will tackle the challenge of designing doped semiconducting polymers whose electronic properties remain stable under repeated ion insertion and mixed ionic-electronic transport. Researchers will combine sophisticated computer simulations, including density functional theory and Holstein modeling, with machine learning algorithms and automated testing systems. The work will be divided into three main areas: first, optimizing materials to achieve high carrier mobility and efficiency across different doping levels; second, designing materials that remain stable under electrochemical and thermal stress through advanced modeling and real-time monitoring; and third, developing methods for creating these materials consistently and safely using non-toxic ingredients. This integrated, closed-loop approach will accelerate development cycles and produce high-performing materials suitable for widespread deployment, directly supporting the DMREF program’s mission of revolutionizing materials innovation through data-driven, collaborative research. Next generation of materials scientists and engineers will be trained through annual workshops on FAIR data principles, AI-driven materials design, and self-driving labs. 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.

NSF Awards · FY 2025 · 2025-09

This I-Corps project investigates the commercial potential of a software package and database system to simulate and predict chemical reactions during semiconductor manufacturing. In current semiconductor manufacturing, forming nanoscale patterns requires hundreds of consecutive, precisely controlled chemical reactions to deposit and etch thin films. As devices shrink and evolve in structure, developing new processes is time intensive and costly. Process optimization often proceeds through trial and error, consuming chemicals and generating waste from scrapped material. A rapid, accurate simulation procedure for these reactions would save considerable time and money and accelerate the development of new products. Providing engineers with real-time simulation data will alleviate many of the most restrictive pain points of the process development burden, providing a significant economic benefit. Furthermore, the understanding gained from accurately programming these chemical mechanisms will generate new insights into nanoscale reaction engineering, promoting the progress of science. Microchips manufacturing is crucial for civilian and military technologies, so the advantage provided by computer simulations will have uses in both commercial and national defense applications. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of computational tools to integrate cutting-edge, vapor-phase, thin film deposition techniques into manufacturing, such as area-selective atomic layer deposition and chemical vapor deposition. Traditionally, patterned features are formed using a repeated sequence of uniform thin film deposition, image generation by photolithography, thin film etching, surface cleaning, and chemical planarization. Lithographic patterning has been a staple of semiconductor manufacturing for decades, but the process is energy intensive, costly, and generates large amounts of chemical waste. Recently, area-selective deposition is being explored to augment or replace some lithographic steps. The approach uses vapor-phase reactants to directly grow patterned thin films, where the pattern is generated by controlled surface chemical reactions. Achieving the necessary control during these reactions is an ongoing challenge. This software tool should allow engineers to accelerate integration of vapor-phase patterning procedures into manufacturing, saving time and reducing waste. This software uses an atomistic algorithm informed by the mechanistic effects of surface chemistry to rigorously simulate area-selective deposition, differentiating it from existing chemical process simulations. 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.

NSF Awards · FY 2025 · 2025-09

This project advances transformative, high-impact STEM education by developing low-cost, scalable, generalizable, and effective learning technologies that equip students for lifelong learning and meaningful collaboration with both humans and AI. Learning-by-teaching is a powerful instructional intervention with significant potential to enhance student learning. This project will design innovative, AI-powered online learning environments that promote the learning-by-teaching approach by enabling students to learn how to teach AI agents. Beyond enhancing academic performance, these AI-powered learning-by-teaching environments aim to develop essential cognitive and collaborative competencies, preparing students to succeed in a future increasingly shaped by AI technologies and human-AI partnerships. This project will design, implement, and empirically evaluate a novel integration of a Teachable Agent, reinforcement learning, and explainable AI to support students' acquisition of both procedural and conceptual knowledge within two online learning environments grounded in the learning-by-teaching paradigm. These environments aim not only to promote learning by teaching but also to help students learn how to teach across two STEM domains and learner populations: middle school algebra and college-level probability. The project team will (1) enhance the Teachable Agent framework with programming-by-demonstration to support integrated learning of both conceptual and procedural knowledge; (2) implement a dual-loop reinforcement learning framework that derives effective pedagogical strategies from expert student demonstrations and aligns them with learner preferences through human feedback; and (3) integrate cognitive and learning theories with generative and explainable AI to produce transparent, pedagogically meaningful explanations. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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.

NSF Awards · FY 2025 · 2025-09

This project addresses one of the fundamental challenges in artificial intelligence (AI): training of neural networks (NNs). NNs are a core component of modern AI models and are widely used in numerous applications across science, engineering, and industry. Training refers to a learning process of AI models. It is notoriously challenging due to the nonlinear and nonconvex nature of NNs. The state-of-the-art methods frequently fail to produce satisfactory results and exhibit unstable behaviors, limiting the accuracy and reliability of AI models. This project will develop effective training methods that significantly improve the training performance over the state-of-the-art, overcoming the current limitations. The broader impacts include creating educational opportunities for undergraduates through a summer research program, developing professional training for K-12 educators on computational mathematics for AI, and establishing public engagement through interactive demonstrations and online resources, all of which will broaden participation in computing and improve public understanding of the role of mathematics in AI. The project develops a novel exploration-exploitation-determination (EED) framework for training neural networks that uniquely combines both local and nonlocal information. Four objectives are: (1) establishing the EED framework for two-layer neural networks by utilizing mathematical analysis of gradient flow dynamics and nonlocal effects; (2) developing a layer-wise training strategy for deep neural networks that sequentially trains each hidden layer; (3) extending the framework to handle noisy and corrupted data through l1-norm minimization; and (4) validating the methods through applications to scientific machine learning tasks including operator learning for PDEs and flow map learning for data-driven discovery of dynamical systems. The computational methods will be rigorously analyzed mathematically, and the resulting algorithms will be made publicly available to enhance reproducibility and maximize impact across multiple scientific disciplines. 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.

NSF Awards · FY 2025 · 2025-09

This three-year project will leverage partnerships among data scientists, researchers, data science education experts and teachers to develop students' data acumen, decision making using data, and use of data tools. The project has three major goals. Goal 1: Create data sets to engage high school students in the Data Life Cycle by cultivating existing partnerships among data scientists, researchers, and data science educators. Goal 2: Co-design curricular materials with teachers and data science education experts that engage high school students in the Data Life Cycle through problem-solving with real data to develop their data acumen. Goal 3: Engage high school students, including those in rural communities, with the Data Life Cycle through problem-solving with real data to develop their data acumen in informal learning spaces. To achieve these goals, the project team will collaborate with researchers and data scientists to gather and prepare real data to design curriculum materials for 9th-12th grade learning spaces. They will co-construct materials with classroom teachers and data science educators to design and implement curricular materials and learning experiences to develop students' data acumen in data science summer camps. Researchers will also support teachers' professional learning to co-facilitate data science camps and disseminate work in their communities. Researchers on the project team will use qualitative approaches including cognitive interviews and observations to glean knowledge regarding student learning and teachers' experiences with co-creation and collaboration of data science materials. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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.

NSF Awards · FY 2025 · 2025-09

The 2025 IEEE Global Communications Conference (IEEE GLOBECOM 2025), held in Taipei, Taiwan on December 8 - 12, 2025. IEEE GLOBECOM is one of the two principal conferences organized by the IEEE Communications Society (ComSoc), held annually to showcase advancements in communications and networking research. Drawing over 2,900 international participants, including academics, researchers, and industry professionals, GLOBECOM facilitates significant knowledge exchange through its technical symposia, industry forums, workshops, and exhibitions. IEEE GLOBECOM 2025 will feature an extensive technical program and industry-focused events. Participation in IEEE GLOBECOM offers students an invaluable opportunity to engage in cutting-edge research, expand their professional network, and gain career-enhancing exposure to academic and industrial leaders in the field of communications and networking. This project supports students from US universities to attend IEEE GLOBECOM 2025 in person. Students will have the opportunity to present their work and be exposed to state-of-the-art developments in the field. They will also have the opportunity to interact with peers from institutions worldwide, meet with senior researchers, and participate in discussions that are likely to shape the future of the field. This grant will target students who will substantially benefit from attending this conference but have limited travel funds. Priority will be given to first-time attendees. 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.

NSF Awards · FY 2025 · 2025-09

This BRITE Pivot project supports research that intends to advance the development of next-generation smart fabrics that can sense, adapt to, and interact with their environments autonomously. While current smart fabrics integrate sensors or actuators into textiles, they typically depend on external hardware or human intervention to function, limiting their potential for true autonomy, energy efficiency, and lightweight design. This research seeks to address a fundamental challenge: how to design textile materials that can physically respond to environmental changes—such as heat or light—without relying on traditional electronic controls or power systems. By embodying physical intelligence directly into the material and structure of the fabric, the project aims to create self-regulating textiles that change shape or stiffness in response to stimuli, enabling applications in wearable healthcare, rehabilitation, human-machine interfaces, and responsive clothing. The research supports the national interest by promoting the progress of science and engineering through interdisciplinary innovation across advanced manufacturing, smart materials, textile engineering, and soft robotics. It also fosters workforce development and public engagement through educational outreach, including programs for K-12 students. By reducing the complexity and cost of smart fabric systems, this work has the potential to impact diverse fields such as personalized medicine, assistive technologies, responsive architecture, and sustainable fashion design. The objective of this BRITE Pivot research project is to uncover the fundamental mechanisms that govern physically intelligent architected fabrics constructed from liquid crystal elastomer-based fibers. The approach in this project combines direct ink writing of liquid crystal elastomer fibers with controlled twisting and weaving to create fabric structures that autonomously adapt their shape and mechanical properties in response to external stimuli. The research is organized into three thrusts: (1) scalable fabrication of high-performance, thermally responsive fibers and their integration into textile architectures; (2) mechanical modeling to understand the coupling between material properties, fiber geometry, and fabric architecture; and (3) actuation studies to characterize and optimize adaptive behaviors such as bending, curling, or stiffness tuning. The project seeks to contribute new theoretical frameworks for modeling complex, twisted fiber structures, and generate predictive tools linking material and structural design to functional output. It also explores how embodied physical intelligence can reduce the need for traditional sensing and control systems. The knowledge gained looks to establish design principles for a new class of intelligent fabrics, with potential to transform how responsive materials are used in wearable and soft robotic systems. 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.

NSF Awards · FY 2025 · 2025-09

This project will investigate the math behind certain quantum-mechanical phenomena that are topological in nature. These phenomena are in the same general ballpark as topological insulators (states of matter with exotic electronic properties that are of modern interest in materials science) and the topological framework for quantum computing. The specific quantum theories in question connect directly to a classical object in the mathematical study of knotted loops of string, called the Alexander polynomial and first introduced in the 1920s. Quantum theories connected to the Alexander polynomial are expected to be easier to understand and to shed light on related quantum theories, e.g. the Chern-Simons theories that are used for topological quantum computing. Preliminary work leading up to this project has indicated that a well-known 1990s-era quantum theory for the Alexander polynomial should satisfy better structural properties than has previously been supposed. This project will elucidate these structural properties and use them to advance our understanding of related quantum theories. This project provides research training opportunities for graduate students. In more technical detail, the project will study the Frohman-Nicas topological quantum field theory (TQFT) for the Alexander polynomial from the point of view of decategorified Heegaard Floer homology. On the Heegaard Floer side, the most general formulations of the theory involve a topological construction called sutured 3-manifolds, but the connection between sutured 3-manifolds and the Frohman-Nicas TQFT, or related Chern-Simons TQFTs, has been largely unexplored up to this point. This project will generalize and reinterpret the Frohman-Nicas TQFT in the setting of sutured 3-manifolds, establishing better functoriality properties than the ones that hold in the non-sutured setting. It will also develop an elaboration of this sutured Frohman-Nicas TQFT that is sensitive to Spin-c structures on 3-manifolds, and work out how the result relates to the type of Spin-c decorated TQFT that plays a prominent role in modern work of Gukov, Putrov, Vafa, and others on nonsemisimple 3d TQFT in the Chern-Simons context. Going beyond the sutured setting, it will show how the sutured Frohman-Nicas TQFT arises from a more fundamental construction assigning a certain representation category to a point. 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.

NSF Awards · FY 2025 · 2025-09

Wave-based imaging techniques are commonly used in geophysical and medical industries to obtain information about an unknown medium by measuring the travel time of reflected waves. For instance, in seismic exploration, seismic energy is used to probe beneath the surface of the Earth and is integral for exploration of economic deposits of oil, gas, or minerals, but also for engineering, archeological, and scientific studies. This is done either passively (using a naturally occurring earthquake) or actively (using a source of seismic energy, such as an explosive charge or seismic vibration) where energy is directed into the Earth. The echoes of seismic waves as they are reflected off of discontinuities in the subsurface are then measured across a measurement area. Similarly, medical ultrasonography is an imaging technique used to create an image of internal body structures such as tendons, muscles, joints, blood vessels, and internal organs. Ultrasound images, also known as sonograms, are created by sending pulses of ultrasonic waves into tissue using a probe. The ultrasound pulses echo off of tissues with different reflection properties and are returned to the probe, which records and displays them as an image. The mathematical foundation behind both of these applications is based on the classical Fermat's principle in physics: a wave takes a path between two locations that can be traveled in the least time. Travel time of a wave defines a mathematical model in which the distance between two locations is measured using a clock instead of a ruler. This type of physically-motivated mathematical framework is commonly studied in the field of differential geometry. This research project develops a stronger understanding of the mathematical theory of seismology and ultrasonography, having a particular emphasis on models with time-dependent material parameters and models that describe anisotropic mediums such as the human body or the subsurface of the Earth. This project focuses on the mathematical theory of indirect measurements arising from seismic exploration and ultrasonography in medical imaging, with particular emphasis on formulating new as well as solving longstanding and challenging geometric inverse problems in these contexts. These problems are formulated in the language of hyperbolic Partial Differential Equations (PDE), with the goal of finding the unknown coefficients of the PDE from a boundary measurement. Since many physical quantities are coordinate invariant, it is conventional to model a terrestrial planet or a human body by a compact, connected Riemannian manifold with boundary. Under these assumptions a fundamental hyperbolic inverse boundary value problem is to recover the unknown geometric structure from the hyperbolic Dirichlet-to-Neumann map (DN-map). This can be accomplished by reducing the PDE-based problem to a geometric problem which carries information about the unknown coefficients of the respective Partial Differential Operator. The project introduces many different reduction methods and solutions to the respective geometric problems, containing three main lines of research: 1) Inverse Problems in Linear Elasticity, introduces elastic inverse problems which can be solved by a reduction to the boundary rigidity and its linearization. These problems go beyond the conventional while insufficient Riemannian formalism. 2) Hyperbolic Inverse Problems with Time-Independent coefficients introduces uniqueness and stability problems for hyperbolic operators on compact and non-compact manifolds. 3) Hyperbolic Inverse Problems with Time-Dependent Coefficients focuses uniqueness problems for general time-dependent hyperbolic inverse problems and invertibility of the light ray transform with partial data. The powerful mathematical methods developed to attack these geometric inverse problems will expand beyond the scope of this project and can be applied for instance in the control theory of PDEs and integral geometry. 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.

NSF Awards · FY 2025 · 2025-09

Multiple perspectives and collaborative input are required to envision transformative solutions to the complex interplay of cascading weather and environmental hazards. Yet civic institutions, scientists, and practitioners are often disconnected from decision-making processes and rarely have opportunities for mutual learning. This project aims to bridge the gap between geoscientists' understanding of natural hazard impacts and practitioners' ability to translate this knowledge into actionable solutions. Partnering with leaders from government, industry, and non-profits across North Carolina, the project will address local and regional environmental challenges through participatory workshops. The workshops will combine learning sessions on key topics with innovative community engagement activities grounded in real-world data and predictive scientific models. By convening individuals with lived experiences of hazard events and those in at-risk areas, the workshops will foster peer learning, scenario-based dialogue, and the co-creation of potential solutions. The project also seeks to catalyze broader public conversations about long-term futures by encouraging deliberation on trade-offs among development, conservation, and population trends, while acknowledging the difficult choices necessary to build system resilience. Findings and tools will be incorporated into student training and professional development for educators who work with the public, including museum staff and librarians. A persistent challenge facing resilience-building efforts stems from the lack of frameworks that integrate scientific expertise with on-the-ground operational and decision-making experience. This project’s integrative framework will advance the translation of Earth system science into actionable insights for local and regional practitioners while shaping a community-driven scientific agenda. This project proposes that a community-driven co-creation process can increase the perceived efficacy of collective solutions, build trust in science-based tools for guiding interventions, and facilitate cross-jurisdictional decision-making to address regional environmental challenges. Phase 1 will investigate how novel engagement strategies affect participants' understanding of complex, interconnected challenges and their perceived self-efficacy to implement co-developed solutions. Scientific modeling and analysis will inform the development of the workshops focused on North Carolina’s mountain and coastal regions, addressing challenges such as constrained infrastructure corridors, varied geologic and topographic conditions, dynamic land-use and land-cover changes, shifting population patterns, and varied landscape literacy. The resulting portfolio of solutions is expected to inform on-the-ground decision-making and contribute to regional resilience strategies. In Phase 2, the project aims to co-develop advanced analytics that respond in real-time to different intervention types and locations, enabling dynamic forecasting of potential outcomes, and supporting adaptive planning across scales. 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.

NSF Awards · FY 2025 · 2025-09

As artificial intelligence (AI) continues to advance and transform society, it is essential that researchers work in direct partnership with teachers to prepare students to understand the world in which they are growing up. Advancing this goal across K-12 education requires a clear understanding of how to introduce AI concepts to elementary school students and how to effectively support teachers in doing so. The PrimaryAI scale-up project advances foundational knowledge in K-12 AI education that leverages immersive problem-based learning pedagogies for upper elementary learners in grades 3 to 5. The project will reach over 5,000 upper elementary students and more than 60 teachers while expanding the research and implementations across multiple states. The project team will partner with teachers from rural communities in Alabama, Indiana, and North Carolina to engage their students in authentic AI-infused problem solving. This approach aims to foster students' interest in science, technology, engineering, and mathematics (STEM) and equip them with fundamental AI knowledge they will need to thrive in the future. The project will investigate key factors that influence successful scaling of an AI education curriculum across multiple state contexts. It will examine the interplay among teacher professional development, localized classroom adaptation, collaborative design methods, and student learning and interest. These elements are central to understanding the conditions for implementation and mechanisms that sustain and expand the use of AI curricula on a large scale in rural upper elementary classrooms. The project will address three primary research questions: (1) What AI concepts serve as entry points for rural teachers to integrate AI into instruction, considering local contexts and individual pathways? (2) What are the impacts on student outcomes for learning, engagement, and STEM interest across rural contexts? and (3) How do local factors in each state's rural context influence the reception, implementation, and outcomes of PrimaryAI? Research questions will be addressed using multiple data sources as part of Design-Based Implementation Research (DBIR) (Fishman & Penuel, 2018). Pre-and post-tests will be used to assess impacts on student learning and interest. The research team has developed assessments for AI concepts, AI planning, computer vision, and machine learning (Chakraburty et al.,2023). To address the first question, the team will collaborate with teachers from rural communities in Alabama, Indiana, and North Carolina. The team will document ongoing collaborative discussions, professional learning processes, teacher designs, and plans for implementation. For the second question, the project will conduct comprehensive analyses of student outcomes using pre-post assessments of AI knowledge and skills, student engagement, STEM interests, observations of student interactions, and student interviews. Additionally, a cross-case analyses to explore commonalities and differences across various rural contexts and implementations will be conducted. To address the third question, a detailed case studies within each rural community to understand local factors such as pedagogical goals, student interests, community priorities, and educational policies is planned. Outcomes will include locally-contextualized versions of the PrimaryAI curriculum, comprehensive teacher professional development guides, case studies that detail successful strategies and challenges, and recommendations for scalability. Ultimately, the project will advance understanding of effective practices and approaches for integrating AI education into rural elementary classrooms. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts, and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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.