Advancing Computational Thinking and Microelectronics Education Through Generative AI Within an Engineering Design Framework

Computational thinking (CT) is a way of solving problems by breaking them down into smaller parts and creating step-by-step solutions or algorithms. There is a critical need to embed CT in science, technology, engineering, and mathematics (STEM) courses beyond computer science to reflect the increasingly computational nature of STEM and to prepare students for future careers. This project will investigate how recent advances in artificial intelligence (AI) can support CT development within an innovative biology curriculum in which students design and program a robotic arm controlled by their own muscle activity. Specifically, the project will focus on how AI tools can assist students in designing algorithms and translating them into computer programs. In response to the CHIPS and Science Act of 2022, the project will also inspire and prepare students to participate in the microelectronics industry, where there is a national need to promote the domestic production of microchips. By interacting with electronic circuits that control the robotic arm and engaging with a microchip simulation, students will have a rich opportunity to learn about the function and utility of microchips. Overall, the project will directly enhance the STEM learning of 20 high school teachers and 500 students.

Recent developments in generative AI provide an exciting yet underexplored opportunity to promote CT education. This project will design innovative AI tools to support CT instruction and employ a rigorous research design to investigate their impact on students' algorithmic thinking. Relatedly, it will develop curricular materials to support AI prompting and explore how student prompting evolves over time. The project will also provide a methodological contribution by establishing validity evidence for an AI prompting tool for high school students. Further, the project will develop a new approach to microelectronics learning, situated within the engineering design process. While most prior work has focused on university students, this project will investigate how the approach impacts high school students. Finally, the project will generate new knowledge on how to prepare and support teachers in fostering CT, AI prompting, and engineering design among their students. To achieve these goals, the project will employ a mixed-methods approach consisting of surveys, interviews, and analysis of user data. AI tools, curriculum materials, and research findings will be disseminated through STEM Resource Finder—an online platform with over 1.4 million users—as well as through workshops for pre- and in-service teachers, conference presentations, and research publications.