This paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers' knowledge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and instructional coaches to ensure the course's relevance and practicality. Using conjecture mapping and memoing, we systematically captured and analyzed insights from the collaborative process. These methods helped us pinpoint essential themes and requirements for effective professional development (PD) that meets the unique challenges and opportunities of teaching about and using AI in K-12 classrooms. Themes included concerns about in-class monitoring for unethical impacts of AI integration and the desire for empowerment in evaluating and selecting AI tools that they can best leverage to meet state and national standards. Educator requirements centered on the creation of quick, easily accessible, and asynchronous learning activities. In addition, educators requested just-in-time AI integration resources and learning opportunities that can be leveraged throughout the year, rather than being limited to PD sessions. This study contributes to AI education by providing a framework for designing teacher professional development programs that are responsive to the evolving educational landscape and the specific needs of K-12 teachers.

The introduction of block-based coding provided teachers a new method to teach computer science to beginners. Since then, numerous programmable learning technologies (PLTs) utilizing this coding modality have surfaced, each bringing their unique properties to the CS education playing field. But while research comparing blocks to text-based coding have identified benefits and drawbacks of each, the same has not been done solely among block-based coding PLTs, specifically regarding their affordances to teachers' learning objectives and instructional needs. In this poster presentation, we present an experiment seeking to understand which properties of block-based coding environments teachers find most attractive to their pedagogical needs. Teachers enrolled in an online graduate-level CS pedagogy course reviewed 6 block-based coding PLTs, reflecting on the extent to which they would use the tools in their classes. We explain their assignment, guidelines given to scaffold their reviews, and our plan for future analysis.

As efforts to incorporate Computer Science (CS) and Computational Thinking (CT) into K-12 classrooms continue to expand, there is a growing need for programs that prepare teachers for the effective teaching and integration of CS and CT into their instruction. An ongoing challenge is preparing current and future teachers to develop the skills and confidence needed to teach and integrate CS and CT. Micro-credentials, designed as a short, focused course, offer opportunities for teachers to build skills and confidence through targeted study. This experience report examines a self-paced online micro-credential developed and implemented within a university-based college of education. The micro-credential was designed to equip both pre-service and in-service teachers with the skills and knowledge necessary to teach and integrate CS and CT into K-12 teaching and learning. We describe the micro-credential, including its structure, sequencing, and content. We then present an exploration of teachers' experiences in the micro-credential. Findings from surveys and CS autobiographies show increases in participants' attitudes, beliefs, and perceptions toward the conceptual and technical aspects of teaching CS, with a particular focus on designing clear and actionable plans for integration. The results from this study provide valuable insights for the development of future CS- and CT-focused micro-credentials.

All students benefit when computer science (CS) materials are accessible, but it is critical for students with disabilities. In order to provide opportunities for all students to be successful, it is important for teachers to be able to evaluate the accessibility of their lessons and technology. One way to evaluate accessibility is the POUR framework. The POUR framework represents what can be Perceived through the senses, how users can Operate a material or technology, how it is Understandable to users, and the overall Robustness. POUR provides a promising way for K-12 CS teachers to evaluate accessibility for their learners. We describe how the POUR framework was used by a cohort of teachers to evaluate VEX 123 for their learners with disabilities. Findings from the teacher POUR analysis revealed that overall, the teachers noted that the VEX 123 provided the necessary range of entryways into coding through its three modalities: The touch coding on the robot itself, the coder cards, and VECcode (the block-based coding environment). At the same time, the teachers indicated that some students with disabilities faced a number of motor and sensory difficulties. Overall, this study showcased a way for teachers to provide insight into the level of accessibility of CS education tools specific to their students' strengths and needs.

As artificial intelligence permeates our lives through various tools and services, there is an increasing need to consider how to teach young learners about AI in a relevant and engaging way. One way to do so is to leverage familiar and pervasive technologies such as conversational AIs. By learning about conversational AIs, learners are introduced to AI concepts such as computers’ perception of natural language, the need for training datasets, and the design of AI-human interactions. In this experience report, we describe a summer camp curriculum designed for middle school learners composed of general AI lessons, unplugged activities, conversational AI lessons, and project activities in which the campers develop their own conversational agents. The results show that this summer camp experience fostered significant increases in learners’ ability beliefs, willingness to share their learning experience, and intent to persist in AI learning. We conclude with a discussion of how conversational AI can be used as an entry point to K-12 AI education.

Women and people from historically marginalized groups are recognized as underrepresented in computer science (CS) courses. We approach this issue by building a K-12 teacher workforce as role models to K-12 students. In this project, we develop a K-12 CS Educator Certificate Program and provide scholarships to 20 Black, Latina, and Native American women in-service teachers. To support these scholars, we provided a community of practice that includes mentoring from experienced CS educators and flexible tutoring from CS content experts. Through these efforts to increase diversity and representation in those who teach CS, we are making strides toward all students seeing themselves in the field of CS.

The ubiquity of AI-based conversational apps such as Siri, Alexa and Google Assistant means more young users are interacting with these apps. The increasing popularity of these conversational applications brings a potential opportunity to attract learners to AI, CS and STEM fields. CS Education researchers need to explore how to leverage this opportunity, in particular to serve learners who are underrepresented in CS and STEM. This experience report describes the design and iterative refinement of a series of two-week summer camps in which 62 predominantly Black students participated in hands-on AI-based learning experiences to design and develop their own conversational AI apps. We discuss the organization of this summer camp experience, including strategies for recruiting from and building trust within the target community, designing professional development for camp facilitators, structuring the camp activities, and encouraging projects that are personally and socially relevant. We share challenges and lessons learned from this AI summer camp in the hopes that they will inform other researchers and practitioners who are interested in designing and deploying similar experiences.