Artificial intelligence (AI) and its teaching in the K-12 grades has been championed as a vital need for the United States due to the technology's future prominence in the 21st century. However, there remain several barriers to effective AI lessons at these age groups including the broad range of interdisciplinary knowledge needed and the lack of formal training or preparation for teachers to implement these lessons. In this experience report, we present ImageSTEAM, a teacher professional development for creating lessons surrounding computer vision, machine learning, and computational photography/cameras targeted for middle school grades 6-8 classes. Teacher professional development workshops were conducted in the states of Arizona and Georgia from 2021-2023 where lessons were co-created with teachers to introduce various specific visual computing concepts while aligning to state and national standards. In addition, the use of a variety of computer vision and image processing software including custom designed Python notebooks were created as technology activities and demonstrations to be used in the classroom. Educational research showed that teachers improved their self-efficacy and outcomes for concepts in computer vision, machine learning, and artificial intelligence when participating in the program. Results from the professional development workshops highlight key opportunities and challenges in integrating this content into the standard curriculum, the benefits of a co-creation pedagogy, and the positive impact on teacher and student's learning experiences. The open-source program curriculum is available at www.imagesteam.org.

The present study adapted the methodology illustrated in a published study for a small sample size [1]. A total of 48 students who were enrolled in an engineering dynamics course were involved in the present study. Multiple linear regression (MLR) and support vector machine (SVM) models were developed and tested. Three criteria were defined to assess the accuracy of the predictive models. The criteria were the number of accurate predicted grade ranges, missing alarm, and maialami. Results indicated that 1) the methodology for developing the predictive models could be employed to different institutions of higher education; and 2) the SVM model may have higher prediction accuracy than the MLR model when the sample size is small.