Exploring AI Hardware Applications in Experiential Learning Environments

Project Members: 1Andrea Ramirez-Salgado, 2Tanvir Hossain, 2Tamzidul Hoque, PhD., 1Swarup Bhunia, PhD., 1Mary Jo Koroly, PhD., Bradford Davey, 1Pavlo “Pasha” Antonenko, PhD. 1University of Florida, 2University of Kansas


  • A matter of national security for the US is supporting the development of the semiconductor and microchip workforce to address the chip shortage and its negative consequences.

  • Higher education institutions are establishing programs to train the necessary workforce and address the CHIPS and Science Act passed by US legislature in 2022.

  • Students pursuing engineering degrees are more inclined to
    pursue software design rather than focus on hardware, primarily
    due to the perceived complexities and the time-consuming nature of electronic manufacturing processes.

  • We have undertaken a project funded by the NSF IUSE program to explore the usability and feasibility of AI-focused hardware activities specifically for first-year undergraduate students across various engineering disciplines.

  • We aim to demystify AI and expand conceptions of ML to cultivate situational interest in computer hardware design and support hardware engineering career choice.

Theoretical Framework

  • We developed a conceptual framework to promote career choice by providing hands-on experiential learning. Career choice is supported by interest development, self-efficacy and outcome expectations.

  • RQ: What are student’s perceptions and practices in an undegraduate AI hardware curriculum?


  • n=22 first-year engineering students are participating in a first implementation of the AI hardware curriculum (6 girls and 16 boys).

  • The curriculum is integrated into an undergraduate course open to students in all engineering disciplines. No previous knowledge or skills are required.

  • We scaffold each activity by offering a hands-on explanation of the hardware device. Afterward, we encourage them to collect data and understand the results.

  • We provide the necessary codes and libraries for each activity.

  • By the end of the semester, students will collaborate in groups to develop a real-world application using the AI hardware approach.

Artificial Intelligence IoT activities – Examples

Preliminary Results

  • Students value the hands-on approach as it helps them quickly grasp AI hardware concepts.

  • They enjoy experimenting with the codes and find it both enjoyable and beneficial.

  • Our approach is seen by students as more meaningful compared to simulations or videos.

  • A suggested improvement is to include additional comments in the code to assist learners with limited or no experience in the programming language.

  • The majority of students have been able to successfully complete all activities.


Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.
Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. Career choice and development, 4(1), 255-311.