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Technological advances in computer vision and machine learning image and audio classification will continue to improve and evolve. Despite their prevalence, teachers feel ill-prepared to use these technologies to support their students’ learning. To address this, in-service middle school teachers participated in professional development, and middle school students participated in summer camp experiences that included the use of Google’s Teachable Machine, an easy-to-use interface for training machine learning classification models. An overview of Teachable Machine is provided. As well, lessons that highlight the use of Teachable Machine in middle school science are explained. Framed within Personal Construct Theory, an analysis of the impact of the professional development on middle school teachers’ perceptions (n = 17) of science lessons and activities is provided. Implications for future practice and future research are described. 

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. 

In the next 50 years, the rise of computing and artificial intelligence (AI) will transform our society and it is clear that students will be forced to engage with AI in their careers. Currently, the United States does not have the infrastructure or capacity in place to support the teaching of AI in the K-12 curriculum. To deal with the above challenges, we introduce the use of visual media as a key bridge technology to engage students in grades 6-8 with AI topics, through a recently NSF funded ITEST program, labeled ImageSTEAM. Specifically, we focus on the idea of a computational camera, which rethinks the sensing interface between the physical world and intelligent machines, and enables students to ponder how sensors and perception fundamentally will augment science and technology in the future. Our 1st set of workshops (summer 2021) with teachers and students were conducted virtually due to recent pandemic, and the results and experiences will be shared and discussed in the conference. 

As part of a professional development experience, inservice teachers were provided with lessons and activities emphasizing computer vision. Lessons focused on image processing, machine learning for visual data, and applications in robotics and visual media. Personal Construct Theory (Kelly, 1955) was used to answer the following research question: After middle school teachers engaged in professional development emphasizing computer vision, what changes in thinking occurred in relation to computer vision? Results showed changes in thinking in the form of a more refined understanding of computer vision and its role in STEM education.