AI curricula are being developed and tested in classrooms, but wider adoption is premised by teacher professional development and buy-in. When engaging in professional development, curricula are treated as set in stone, static and educators are prepared to offer the curriculum as written instead of empowered to be leaders in efforts to spread and sustain AI education. This limits the degree to which teachers tailor new curricula to student needs and interests, ultimately distancing students from new and potentially relevant content. This paper describes an AI Educator Make-a-Thon, a two-day gathering of 34 educators from across the United States that centered co-design of AI literacy materials as the culminating experience of a year-long professional development program called Everyday AI (EdAI) in which educators studied and practiced implementing an innovative curriculum for Developing AI Literacy (DAILy) in their classrooms. Inspired by the energizing and empowering experiences of Hack-a-Thons, the Make-a-Thon was designed to increase the depth and longevity of the educators' investment in AI education by positively impacting their sense of belonging to the AI community, AI content knowledge, and their self confidence as AI curriculum designers. In this paper we describe the Make-a-Thon design, findings, and recommendations for future educator-centered Make-a-Thons.

In this experience report, we describe an Artificial Intelligence (AI) Methods in Data Science (DS) curriculum and professional development (PD) program designed to prepare high school teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI to support their integration of AI methods into existing STEM classrooms. The curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Networks, and Transfer learning that follow a scaffolded learning progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, interactive web-based tools, and inspecting and modifying the code used to build, train and test AI models within Google Colab notebooks. The participants in the PD program were secondary school teachers from the Southwest and North-east regions of the United States who represented a variety of STEM disciplines: Biology, Chemistry, Physics, Engi-neering, and Mathematics. We share findings on teacher outcomes from the implementation of two one-week PD workshops during the summer of 2021 and share suggestions for improvements provided by teachers. We conclude with a discussion of affordances and challenges encountered in preparing teachers to integrate AI education into disciplinary classrooms.

This paper describes an AI Book Club as an innovative 20-hour professional development (PD) model designed to prepare teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI that are foundational to developing AI-literate citizens. The design of the intervention was motivated by a desire to manage the cognitive load of AI learning by spreading the PD program over several weeks and a desire to form and maintain a community of teachers interested in AI education during the COVID-19 pandemic. Each week participants spent an hour independently reading selections from an AI book, reviewing AI activities, and viewing videos of other educators teaching the activities, then met online for 1 hour to discuss the materials and brainstorm how they might adapt the materials for their classrooms. The participants in the AI Book Club were 37 middle school educators from 3 US school districts and 5 youth-serving organizations. The teachers are from STEM disciplines as well as Social Studies and Art. Eighty-nine percent were from underrepresented groups in STEM and CS. In this paper we describe the design of the AI Book Club, its implementation, and preliminary findings on teachers' impressions of the AI Book Club as a form of PD, thoughts about teaching AI in classrooms, and interest in continuing the book club model in the upcoming year. We conclude with recommendations for others interested in implementing a book club PD format for AI learning.