Agentic AI, where AI systems have their own forms of agency, will present some of the most critical challenges that computing education will face, including regulatory gaps and amplified cascading social and ethical impacts. This working group proposes a landscape analysis of the ethical and societal implications of using Agentic AI in higher computing education. By exploring emerging literature and use cases of Agentic AI, we aim to contribute a timely landscape study exploring 1) the emerging challenges and opportunities associated with Agentic AI, 2) how higher education is beginning to adopt and use Agentic AI and the resulting ethical and societal impacts, and 3) the implications (e.g. challenges, opportunities, limitations) of integrating Agentic AI in computing education. The expected outputs are: 1) a protocol and literature scoping review of Agentic AI in education, and 2) an analysis of current practices and use cases in computing education. Together, these outputs aim to identify emerging patterns, use cases, key challenges, and principles to support the ethical and responsible integration of Agentic AI in education.

Generative AI (GenAI) presents societal and ethical challenges related to equity, academic integrity, bias, and data provenance. This working group will consider the ethical and societal impacts of GenAI in higher computing education. In this paper, we outline the goals, methodology and expected deliverables of the working group. In particular, we will carry out a systematic literature review to address a wide set of issues and topics covering the rapidly emerging technology of GenAI from the perspective of its ethical and social impacts, we will provide an evaluation of university policies on the adoption and guidelines for use of GenAI for computing education and develop a framework to outline the ethical and societal impacts of GenAI in computing education. This work synthesizes existing research and considers the implications for educational and professional codes of ethics.

Computer science (CS) education policy efforts have accelerated since 2016 through the work of various governmental, advocacy, and CS-focused organizations. CS policy implementation is typically led by CS education state supervisors (CSEdSS) at state education agencies (SEA), whose responsibilities may encompass training CS teachers, allocating resources, and informing teacher certification. Despite efforts to expand K-12 CS education through a set of 10 nationally-recommended policies, equity issues persist in terms of access for historically marginalized students to learn CS. Moreover, while states may adopt the same policy, each state has their own model of state education governance (SEG). These models determine authority and accountability - how education decision-making and policies are made and implemented. This study explores the relationship between SEG models and impact of CS education policy implementation by exploring the average rate of growth in access to high school (HS) CS and percent of CS education policies adopted across SEG models. Data sources include publicly available data of secondary CS access and CSEdSS survey and focus groups. Preliminary findings indicate the need to consider SEG models when enacting CS education policy to balance accountability with authority when enacting CS education policy related to expanding equitable and accessible K-12 CS education.

To address the issue of meaningfully including students in elementary computer science (CS) education, we design, implement, and evaluate an innovative professional development (PD) and community of practice (CoP) model for supporting elementary general and special education teacher dyads to apply inclusive pedagogy in their CS instructional practice. This project aims to improve interest, ability beliefs, and academic outcomes for elementary students with disabilities in CS learning. This online PD/CoP supports teachers in learning, implementing, and reflecting on how to best integrate Universal Design for Learning and High-Leverage Practices within CS instruction. Membership in this PD/CoP is predicted to increase the participation of students with disabilities in elementary CS instruction by increasing the teachers' CS content and pedagogical knowledge, self-efficacy, and inclusive mindsets and practice.

Tangential to the efforts to bring computer science (CS) into K-12 education, there has been increasing recognition of the critical role of data science (DS) in preparing future citizens to be able to gather, analyze, and represent data. With only 51% of K-12 schools offering CS, however, and the critical need for students to engage in DS practices, there is the need to examine ways to integrate DS in other subjects. Our study explores the current landscape of DS in methods and content courses within preservice teacher pathways. This poster outlines a study in its preliminary stages that explores how faculty teaching math, science, and social studies methods and content courses in colleges of education: a) define DS, b) conceptualize DS as related to their course content, c) make connections between DS, CS, and/or computational thinking (CT). Taking a participatory design approach, this study will also explore research-based approaches to building the capacity of preservice faculty in DS to advance the practice of teaching CS in a scalable way to expand access in equitable ways to CS and CT.

In response to the persistent diversity gap that exists in the computing field, we implemented a five-day professional development (PD) workshop for elementary teachers on integrating computational thinking (CT) and culturally responsive teaching (CRT) practices into their existing science instruction. An explicit focus of this PD was to engage teachers in analyzing CT- and CRT-integrated science classroom scenarios (i.e., vignettes). Our analyses of video and written data of teachers' vignette responses indicate that while their understanding of CT increased as a result of the PD, the teachers need additional support around CRT. We suggest that future studies explore how teachers define and enact CRT, especially within their unique contexts.

K-12 computer science (CS) teachers are often the only teachers of the subject at their school. Many school-based administrators and personnel lack the content knowledge to support their ongoing professional growth. How then can an ecosystem of support be developed to support K-12 CS teachers? We have created several tools aligned to the CSTA Standards for CS Teachers that support administrators, instructional specialists, and teacher leaders to provide evidence-based feedback and promote the ongoing development of CS teachers at their schools. These tools, including a CS coaching toolkit and instructional practice evidence guide, have the potential to drive impactful, job-embedded development.

What teacher knowledge and skills are necessary for effective instruction of K-12 computer science? In recent years, standards and a national framework have delineated universal learning student outcomes in K-12 computer science (CS) education, but there has been less focus and coherence on what is required of teachers to prepare their students to equitably meet these learning outcomes. The 2020 CSTA Standards for CS Teachers were designed to meet this need. In this BoF session, we will provide a platform to discuss the evidence base aligned to each of the five teacher standards: (1) CS Content Knowledge and Skills, (2) Equity and Inclusion, (3) Professional Growth and Identity, (4) Instructional Design, and (5) Classroom Practice. Participants will share their knowledge and experience in a facilitated conversation organized around focal questions for each standard.