Project Profile

The exponential expansion of artificial intelligence (AI) has created a significant demand on future AI workforce development. Several key challenges exist hindering the widespread incorporation of AI into K-12 curricula, including the lack of teacher development in AI and a comprehensive pedagogical framework for K-12 AI education. This project aims to engage high-school students from North Dakota and southern Alabama, two geographical areas with large populations from historically marginalized groups, in the learning of AI by implementing and examining a culturally relevant project-based AI-integrated learning framework. The framework connects AI learning with students' learning experiences in science, technology, engineering, and mathematics (STEM) classes, fostering the growth of their interdisciplinary problem-solving skills. To develop students' individual interest in AI, their learning is situated within the context of personal experiences. This proposal was received in response to the Dear Colleague Letter (DCL): Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (NSF 23-097) and 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.

The project includes two components: (1) training of high-school teachers, and (2) teachers' classroom implementation. Seventeen teachers will participate in small group and individual hands-on practices, immersive workshops, reflection and discussion, and presentations. Researchers will carry out mixed-method design research examining teacher training outcome. With scaffolding and support from researchers, teachers will develop and teach AI-STEM integrated lessons in biology and pre-calculus at their own high schools in North Dakota and southern Alabama. Multimodal data collection and analysis will be used for researching the perceptual, cognitive, and kinesthetic processes about students' problem-solving skills, interdisciplinary knowledge use, and classroom participation, as well as how these processes affect students' STEM and AI learning and interest development. This research has significant potential to contribute to our knowledge on how to effectively support historically marginalized students' interest in and learning of AI.

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.