Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse high schools. The goal of this project is to engage high-school students and teachers from underresourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow.
Projects
Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse high schools. The goal of this project is to engage high-school students and teachers from underresourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow.
Data literacy is the ability to ask questions, analyze, interpret, and draw conclusions from data. As the world and the workplace become more data-driven, students need to have stronger data literacy across multiple disciplines, including science. This project uses an instructional framework, Data Puzzles, to investigate how to support middle grades teachers learning to include data literacy in their science teaching. Data Puzzles integrate mathematical and computational thinking with ambitious science teaching instructional practices and contemporary science topics. Students engaging with Data Puzzles resources can analyze real-world climate science data using web-based data analysis tools to make sense of science phenomena and develop data literacy.
The United States faces the critical need to prepare students and the future workforce for advances in Artificial Intelligence (AI). This project will develop curriculum that will engage middle-school students in learning science and basic AI concepts and in developing related career interests.
Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse high schools. The goal of this project is to engage high-school students and teachers from underresourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow.
Data literacy is the ability to ask questions, analyze, interpret, and draw conclusions from data. As the world and the workplace become more data-driven, students need to have stronger data literacy across multiple disciplines, including science. This project uses an instructional framework, Data Puzzles, to investigate how to support middle grades teachers learning to include data literacy in their science teaching. Data Puzzles integrate mathematical and computational thinking with ambitious science teaching instructional practices and contemporary science topics. Students engaging with Data Puzzles resources can analyze real-world climate science data using web-based data analysis tools to make sense of science phenomena and develop data literacy.
With recent advances in artificial intelligence (AI), the United States needs to develop a diverse workforce with strong computational skills and the knowledge and capability to work with AI. Recent studies have raised questions about the extent to which youth are aware of AI and its application in industries of the future that may limit their interest in pursuing learning that lead toward careers in these industries. To address this challenge, learning trajectories (LTs) will be developed and researched for AI concepts that are challenging for middle and high school students. The project will design and pilot test learning activities and assessments targeting these concepts based on the LTs, offer teacher professional development on the LTs and related activities, and research the effectiveness of the LT-based activities when implemented by teachers during the regular school day.
Data science is fast becoming a dominant part of scientific, economic, and social scientific reasoning, as well as a necessary perspective for making life decisions that rely on data. But there is little work in either research or development with students younger than 2nd grade, and even less focusing on children not yet in kindergarten. By expanding knowledge in an emergent content area where young children are currently underserved, this set of three workshops will build capacity to create rigorous, equitable, inclusive, and culturally responsive data science teaching resources, a necessity for school readiness in the early childhood years.
STEM learning is a function of both student level and classroom level characteristics. Though research efforts often focus on the impacts of classrooms level features, much of the variation in student outcomes is at the student level. Hence it is critical to consider individual students and how their developmental systems (e.g., emotion, cognition, relational, attention, language) interact to influence learning in classroom settings. This is particularly important in developing effective models for personalized learning. To date, efforts to individualize curricula, differentiate instruction, or leverage formative assessment lack an evidence base to support innovation and impact. Tools are needed to describe individual-level learning processes and contexts that support them. The proposed network will incubate and pilot a laboratory classroom to produce real-time metrics on behavioral, neurological, physiological, cognitive, and physical data at individual student and teacher levels, reflecting the diverse dynamics of classroom experiences that co-regulate learning for all students.
The rapid onset of AI, and generative AI tools such as LLMs, amplify the need for AI literacies, including concepts, practices and ethics, for K-12 schools. Some AI literacy resources, such as AI4K12 and AI4ALL, have emerged, but it may be challenging for schools, particularly those in small districts, to navigate these resources. Furthermore, researchers need further guidance on how to support schools for AI literacy. These challenges for schools and researchers include how to coordinate planning across teachers, school leaders and researchers, how to implement across grade levels, classrooms, and content areas; how to provide training and preparation time to support lesson design and implementation; and how to support teachers in their own AI literacy. To address these needs, district leaders and teachers from Forest Park School District and researchers from the University of Illinois Chicago will engage in a one-year research practice partnership development to build a long-term RPP, co-design an AI literacy curriculum, and support professional development to implement the curriculum.
The growing importance of data, data science and artificial intelligence (AI) in education, work, and personal and civic life has increased the need for all U.S. students to develop data literacy, statistical reasoning, and computational thinking skills. However, most middle school students—especially those with learning disabilities (SLD)—receive limited or no instruction in these areas. Data science and AI instruction is often limited to high school settings, narrowly framed within mathematics or science, and rarely designed with the flexibility to support learner variability. The purpose of this project is to develop and refine Data Adventures, a series of open-access, modular, and instructional experiences units designed to introduce middle school students to data literacy, computational thinking, and digital storytelling, while also promoting critical understanding of AI and its role in education, work technology, and everyday life.
Coding is a key part of computer science, and promoting opportunities that engage learners in coding is vital to the U.S. workforce development. This project builds upon prior research that created a free coding app, OctoStudio, which is widely available for use in elementary and secondary schools. The goals of the project are twofold: First, the team will develop and design features that broaden the technology so that it is more accessible for more users. Second, the team will explore the usability of these new features across potential users. The new features will have potential to allow blind and low vision users to meaningfully engage in coding, which ultimately benefits society by broadening the STEM workforce and bringing coding to a greater population of students.
As STEM education researchers work to improve STEM teaching and learning in schools and districts across the nation, rural communities are often overlooked. There is a definite critical need for STEM education research focused on rural communities. Rural schools typically have less funding for STEM programs, have trouble recruiting and retaining quality STEM teachers, and have less access to STEM learning opportunities. Yet, rural communities possess an abundance of ingenuity, resourcefulness, and collective problem-solving skills. This project works to address this need by bringing together researchers, rural educators, and workforce leaders in rural communities to support the mutual exchange of knowledge and learning around pressing problems in rural K-12 STEM education, understanding rural ingenuity within teaching STEM, and STEM education's connection with the local workforce.
As artificial intelligence (AI) becomes increasingly embedded in the technologies used by both students and teachers, it is essential for them to understand how to be safe while using AI. Furthermore, AI and cybersecurity technology together are better at detecting malicious activities than conventional security systems. The need to blend the two disciplines into a single, integrated curriculum for K-8 education is highlighted by the interconnectedness of AI and cybersecurity as complementary systems. This project will "plant the seeds" of these literacies by spiraling content on topics from computer programming, internet fundamentals, and introduction to data and AI along with cybersecurity topics in small doses throughout students' K-8 education. This project will lay the foundation for the students to eventually develop a comprehensive understanding of how different technologies work and interact.
While simulations are powerful tools for scientific inquiry, most students need scaffolding to engage productively in simulation-based inquiry. This project will develop and study an automated feedback system designed to support middle school students' simulation-based inquiry into wildfires, floods, and hurricanes. The system, called Hazbot, will leverage advanced artificial intelligence (AI) technologies—including machine learning and large language models (LLMs)—to provide timely, personalized feedback as students investigate the three different natural hazards.
As artificial intelligence (AI) becomes increasingly embedded in the technologies used by both students and teachers, it is essential for them to understand how to be safe while using AI. Furthermore, AI and cybersecurity technology together are better at detecting malicious activities than conventional security systems. The need to blend the two disciplines into a single, integrated curriculum for K-8 education is highlighted by the interconnectedness of AI and cybersecurity as complementary systems. This project will "plant the seeds" of these literacies by spiraling content on topics from computer programming, internet fundamentals, and introduction to data and AI along with cybersecurity topics in small doses throughout students' K-8 education. This project will lay the foundation for the students to eventually develop a comprehensive understanding of how different technologies work and interact.
Rapid changes in computing, especially with advances in artificial intelligence, are reshaping the future needs of society and the demands on the STEM workforce. More than ever, computer science (CS) education is critical for all children. Many schools are looking for ways to introduce CS skills and thinking in the elementary grades. Whereas some initiatives have focused on coding as its own endeavor, not integrated with subjects like mathematics, science, or literacy, developers and researchers are increasingly exploring ways that programming and computational thinking (CT) can be integrated into core content. This project will design and study resources that build teacher capacity to integrate CS/CT into mathematics by building on the investigators' prior work developing integrated Math+CS modules in grades 2-5.