Artificial intelligence (AI) technologies are rapidly reshaping teaching and learning in K–12 education. Yet, little is known about the factors influencing K–12 computer science (CS) teachers' attitudes towards AI technologies or their integration of AI-related instruction. This exploratory study examines the relationship between CS teachers' familiarity with generative AI, their attitudes toward AI technologies in K-12 education, and their integration of instruction on AI concepts into CS courses. Using survey data from 632 K–12 CS teachers across Pennsylvania, we explored (1) how familiarity with generative AI relates to CS teachers' support for AI integration in K-12 instruction, efficacy to teach AI concepts, and AI instructional time in CS courses; and (2) whether teachers' familiarity with AI and AI instruction time vary across their background characteristics. Finding 1. Correlational analyses revealed moderate to strong positive relationships between teachers' familiarity with AI and their support for AI integration in K-12 domains (r = .35), efficacy to teach AI concepts (r = .53), and hours of instruction on AI principles in CS courses (r = .32). Finding 2. Regression models indicated that male-identifying teachers reported significantly higher familiarity with generative AI technologies and greater integration of instruction on AI concepts than female-identifying teachers. Additionally, high school teachers reported dedicating more instructional time to AI concepts in their CS courses than CS teachers at other grade levels. Consistent with prior work on computing technology adoption, findings provide early evidence that teachers' familiarity with AI is closely associated with affective factors that shape their support for and implementation of AI-related instruction.

The Computer Science (CS) Professional Development (PD) Passport Alliance has created an addition to the Computer Science Teachers Association (CSTA) member platform that provides the capability for CSTA members to track their CS PD-related activities in one convenient place. As part of our research effort supporting the platform development, one of our research questions was: Among teachers, what features or practices are associated with trusting in software that contains professional personally identifiable information (PII)? We conducted an exploratory mixed methods study (teacher interviews n = 17, teacher survey n = 495, program evaluation survey n = 494) to determine what enables trust in software that stores professionally identifiable information and which features were most important to teachers future use of the platform. We provide recommendations for the project implementation team (such as adding multi-factor authentication to the platform) to mitigate risks of a data breach (which our survey indicated would impact 98% of the respondents' perceptions).

Although there is extensive research on what makes K-12 teacher computer science (CS) professional development (PD) effective, little attention has been given to how PD providers prefer to collect and report data. A platform that met the needs of teachers while also simultaneously meeting the needs of funding bodies could be powerful in answering questions about participation and experiences in CS PD. Our research question for this study was: Which features and types of data do teachers find most important to include in a platform designed to record data related to their engagement with PD? We used an exploratory-sequential mixed methods approach that included focus groups and a survey created from an analysis of the data from the focus groups.

To understand secondary teachers of computer science's perspectives on barriers to implementing equitable CS instruction during the COVID-19 pandemic, the research team administered a survey to the CS for All Teachers virtual community of practice in Spring 2021 and examined responses from middle and high school teachers of CS (n = 349). We used the CAPE framework (Capacity, Access, Participation, and Experience) to ground our analysis of survey responses. Key Finding 1 is that secondary CS expansion is limited due to competing priorities related to college preparation and standardized testing requirements, which do not usually include CS (Capacity; Access). Key Finding 2 is that student access to broadband at home creates barriers to secondary CS learning (Access; Participation; Experience). These findings reinforce the evidence base that expanding K-12 CS education (before, during, and after COVID-19) faces significant barriers due to gaps and disparities in CS education policies and inequities in students' access to necessary technology.