2026 ASEE Annual Conference & Exposition

For most students, exposure to Artificial Intelligence (AI) occurs through software applications such as chatbots and recommendation systems. While these interactions are pervasive, they offer only a partial view of a rapidly growing field increasingly shaped by embedded intelligence, where AI algorithms run on microcontrollers and sensor networks to enable real-time interaction with the physical world (Farrelly & Baker, 2023). Preparing students to participate in this emerging field requires a shift toward hands-on engagement with intelligent systems built on microelectronics that are both tangible and locally relevant. Our project, funded by the NSF Discovery Research PreK-12 (DRK-12) program, is co-designing a high school curriculum that integrates core principles of AI and embedded systems through experiential learning. During its first two years and before wide classroom implementation, the project emphasizes early collaboration with teachers and informal educators to establish a strong curricular foundation. The team is partnering with high school teachers from Florida, Texas, and Kansas, as well as educators from community learning organizations, whose contributions ensure that the curriculum remains adaptable across both formal and informal learning environments.
The curriculum enables students to engage with foundational concepts in AI and embedded intelligence through real-world applications such as environmental sensing and transportation safety monitoring, fostering the development of technical fluency and analytical problem-solving skills. Each activity is grounded in altruistic (Lakin et al., 2021), community-relevant challenges that invite students to apply engineering principles in ways that improve local conditions and support others. The research further explores how participation in these community-oriented activities may relate to students’ developing sense of agency and engineering identities (Pattison et al., 2020). Guided by a design-based implementation research (DBIR) framework, our team built on insights gained from previous NSF-funded projects to design the first version of the curriculum. The accompanying guides were shared with participating teachers and informal educators for review and feedback. Input was collected through shared documents, synchronous meetings, interviews, and classroom and program observations conducted after pilot implementations in both school and community settings. These multiple forms of engagement allowed the research team to examine how participants interpreted the role of embedded intelligence and microelectronics in engineering education and to refine the curriculum based on their experiences and recommendations.
Early findings suggest that educators value project-based approaches that make AI and embedded systems instruction purposeful and connected to students’ local contexts. Their feedback highlighted opportunities to extend sensor-based projects across subjects such as physics, environmental science, and computer science, creating stronger interdisciplinary ties within existing curricula. Building on these insights, the next phase will engage approximately 500 high school students to examine how participation in embedded intelligence activities shapes their understanding of AI concepts and their developing engineering identities. This paper and poster presentation will share key outcomes from the first year of co-design, including representative modules, co-design strategies, and lessons learned from adapting the curriculum across formal and informal settings. This work advances a scalable model for secondary education that links AI technical skill development with socially meaningful engineering practice.