2026 ASEE Annual Conference & Exposition

WIP: Leveraging Embedded Systems and Edge Intelligence to Enhance High-schoolers’ Conceptual Change about AI

Presented at Computers in Education (CoED): Poster Session - Division Special Events (1 of 4) -- M208

Embedded systems are foundational to electrical and computer engineering, requiring integrated knowledge of both hardware and software. As Artificial Intelligence (AI) becomes increasingly embedded in everyday systems, it is essential for future engineers to understand AI not only as algorithms but also as practical, system-level implementations. However, most pre-college programs emphasize computational thinking and AI algorithms rather than hands-on, embedded applications.

This work-in-progress presents an exploratory three-hour curriculum that introduces high school students to Embedded Systems, Tiny Machine Learning (TinyML), and AIoT through hands-on experience with an ESP32-based learning board with breadboard, a battery, five different sensors, and an OLED monitor. Using Arduino and Edge Impulse software, students witnessed the full TinyML workflow, from data collection and preprocessing to model training and deployment. This curriculum aims to help students conceptualize AI as a data-driven computational process within physical systems. Guided by the Cognitive Reconstruction of Knowledge Model (CRKM) of conceptual change, which highlighted the importance of affective and motivational factors in conceptual change process, this study investigates whether students revise their prior AI conceptions toward broader, system-level understanding.

One hundred and seventeen high-schoolers from four schools in the southeastern United States participated in the curriculum, with thirty four students completing the pre- and post-surveys. Quantitative analysis using paired t-tests and item-level diagnostics revealed meaningful gains in understanding AI’s technical functioning (e.g., functioning without internet) and its societal roles. While the results are preliminary, they suggest that integrating Embedded Systems and Edge Intelligence can serve as an effective gateway for developing foundational AI literacy in pre-college engineering contexts. This paper presents the detailed curriculum design, exploratory assessment results, and a path for future psychometric validation of AI conception survey, contributing to the growing body of research on integrating Edge Intelligence into pre-college engineering education.

Authors

Woorin Hwang http://orcid.org/0000-0001-7260-3431 University of Florida [biography]

Woorin Hwang is a Ph.D. candidate in Educational Technology at the University of Florida. Her research is focused on integrating Artificial Intelligence (AI) in teaching and learning, with ongoing projects related to AI literacy, Internet of Things (IoT), and engineering education. Prior to joining the University of Florida, Ms. Hwang worked as a teacher and instructional designer in South Korea, where she led several projects in learning content design and the development of educational systems.
Clelia Perez-Medina University of Florida [biography]

Clelia Perez-Medina is a Ph.D. student in Educational Technology at the University of Florida and a Fulbright Scholar. She holds a Master’s degree in Marketing and Advertising, a specialization in Consumer Psychology, and a Bachelor’s degree in Graphic Design. With over ten years of academic experience, she has taught marketing courses at several universities in Colombia. From 2022 to 2024, she served as Director of Graduate Programs in Consumer Psychology. Her current research focuses on ethics in artificial intelligence in education, particularly on ethical frameworks and their application within educational contexts.
Yessy Eka Ambarwati http://orcid.org/https://0009-0002-3547-5869 University of Florida [biography]

Yessy Eka Ambarwati is a Fulbright Scholar and Research Associate at the University of Florida, pursuing a Master’s in Educational Technology. Her research centers on AI hardware-based learning and game-based instructional design in STEM education, with contributions to an NSF-funded project on microelectronics and Edge AI curriculum development.
Anany Sharma University of Florida
Dr. Pavlo Antonenko [biography]

Pavlo "Pasha" Antonenko is an Associate Professor of Educational Technology at the University of Florida. His interests focus on the design of technology-enhanced learning environments and rigorous mixed-method research on the effective conditions for tec
Andrea Ramirez-Salgado http://orcid.org/https://0000-0002-0001-2672 University of Florida [biography]

Andrea Ramirez-Salgado is an Instructional Assistant Professor in Engineering Education at the University of Florida, where she teaches courses in artificial intelligence and data science. Her research examines how instructional practices shape students’ identities in engineering and computer science, and how these
identities affect their career goals and persistence. She is particularly committed to creating inclusive, engaging learning environments that support diverse learners.
Lauren Eutsler University of North Texas [biography]

Dr. Lauren Eutsler is an Associate Professor of Learning Technologies and Director of the Texas Center for Educational Technology at the University of North Texas. Her research examines technology integration, artificial intelligence, and literacy in K–16 learning environments. She currently co-leads a NSF-funded project that engage teachers and students in hands-on STEM and AI learning through microelectronics and engineering design.
Dr. Swarup Bhunia

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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For those interested in:

Broadening Participation in Engineering and Engineering Technology
computer science
engineering
engineering technology
Pre-College