Generative AI is increasingly used in learning environments, but most systems focus on generating responses rather than supporting interaction. This limitation is especially important for blind and visually impaired (BVI) learners, who rely on non-visual ways to navigate and understand programming tasks. We present findings from a co-design study with BVI high school students exploring expectations for AI assistants in expressive computer science learning. Participants described how AI could support accessible interaction, guide learning, and empower learner agency. We derive two design principles for inclusive AI assistants: (1) situated multimodal interaction and (2) scaffolded iterative interaction. These principles shift the focus from adapting outputs (e.g., audio) to designing interactions that support how learners navigate and engage in the CS learning context. Our work highlights the importance of interaction design in accessible AI and the value of involving BVI learners in the design of AI-powered educational technologies.

Expressive computer science (CS) learning environments teach coding through the creation of an artifact, such as audio or video output.EarSketch is an expressive CS learning environment designed to teach computing through music production, mixing and arranging sounds using code.In this paper, we explore the accessibility challenges of using EarSketch for learners who are Blind and Visually Impaired (BVI).We present key findings from co-design studies with teachers and students at an institution specializing in BVI education, focused on gathering both groups' unique perspectives about EarSketch's ability to support teachers' curricula, students' workflows using the system with accessibility software, and challenges faced by users who are BVI.

To engage diverse populations of students who may not self-select into computing courses, a curriculum for a middle school music technology + computer science course that addresses learning standards for both subjects was developed and deployed. Students who engage with the curriculum learn modern music production techniques and computational thinking concepts. This is through a mix of traditional approaches to music technology education (digital audio workstations) and computational approaches via a culturally relevant learning platform that introduces students to coding through music production and remixing. This poster reflects on the last two years of curriculum design and deployment, teacher training, and student and educator engagement and feedback to provide insight into the teaching (and learning) of computational thinking in the music technology classroom.

Computer science educators often use multiple creative computing platforms to motivate and support students learning computer science. Arguably, we understand little about the complementary ways in which the various platforms build on students' prior experiences. This study compares two CS+music platforms used by middle school students in a summer camp to understand the unique affordances of each platform at activating and building upon prior music and computing experiences. We assess interest formation through pre and post student surveys and via interviews on the final day of the camp. The findings suggest that using different approaches to CS+music platform design may help engage students with different levels of prior music and coding experience.

A number of computing departments have adapted studio-based pedagogy for teaching Human-Computer Interaction (HCI) to help students develop creative design skills. These efforts have shown promise, however, some challenges that emerge when integrating studio-based pedagogy into computing programs have been neglected by researchers. This research focuses on these challenges in the implementation and evaluation of a studio-based approach integrated into three HCI-related courses. This study is based on 6 classes, 44 interviews with students, observations, and interviews with members of the teaching team. Findings supported many positive outcomes from previous studio-based implementations. Nevertheless, students felt the change in pedagogy frustrating and it may have hindered their creativity, motivation, and engagement. This paper highlights these conflicting narratives, comparing computing culture with studio-based pedagogy. We conclude with considerations for future implementations.

Educators have long claimed that pedagogical content knowledge (PCK), ways of presenting a subject that make it comprehensible to others, is a critical element of student academic success. This paper presents an exploratory study finding that PCK significantly correlates with students' computer science (CS) content knowledge acquisition while teacher CS content knowledge does not. This study analyzes systemic factors comprising the theory of change for the EarSketch learning intervention including classroom- and student-level factors that contribute to shifts in students' attitudes toward computing and ultimately to students' intentions to persist in future computing endeavors and students' CS content knowledge. We present the results of a multi-level modeling analysis and offer an exploratory approach to measuring CS PCK along with recommendations to improve the sensitivity of the method.

Educators have long claimed that pedagogical content knowledge (PCK), ways of presenting a subject that make it comprehensible to others, is a critical element of student academic success. This poster presents findings from a preliminary study of Computer Science Principles teachers that demonstrate a significant correlation between teacher PCK and student content knowledge. The study describes the measures used to assess PCK, operationalizes the mechanisms for measuring teacher pedagogical content knowledge, shares the results from a multi-level modeling analysis, and identifies methodological improvements for follow-up research. The findings from this initial study show correlations of teacher PCK with student content knowledge while accounting for other teacher- and student-level variables across multiple schools.

The expansion of computer science into more classrooms invites researchers and evaluators to shift their focus from predominantly measuring student-level factors to measuring both student- and classroom-level variables. Research presented in this article uses multi-level modeling to study student-level factors within the larger context of classroom-level factors. Specifically, we analyze EarSketch, a collaborative and authentic learning tool, that introduces students to programming through music remixing, has previously been shown to increase student engagement, and increases learners' intentions to persist in computing. This article presents classroom implementation frameworks commonly used in math and science education but rarely, if ever, applied to computer science. The results from a multi-level modeling analysis show that classroom implementation correlates with students' intentions to persist in computing but may not be related to student attitudes toward computing or content knowledge acquisition. Further analysis reveals that one of the five classroom implementation factors, elaboration, emerges as the most salient. This article triangulates these results with qualitative findings from school administrators and teachers, and the article concludes by theorizing how classroom implementation frameworks may be adapted to meet the unique needs of computer science teachers, learners, researchers, evaluators, and curriculum developers.

STEAM education is an approach to engage students in STEM topics by prioritizing personal expression, creativity, and aesthetics. EarSketch, a collaborative and authentic learning tool, introduces students to programming through music remixing, has previously been shown to increase student engagement, and increases learner's intentions to persist in computing. The goal of EarSketch is to broaden participation in computing through a thickly authentic learning environment that has personal and real world relevance in both computational and music domains. This article reports a quasi-experimental study suggesting that an authentic learning environment predicts increased intentions to persist via identity/belongingness and creativity. We ran a path analysis that exposed the creativity subscales, and this analysis reveals that "sharing" is the one creativity sub-construct that predicts increased intention to persist. This work makes a significant contribution to computer science education by revealing how an authentic STEAM curriculum affects student attitudes and knowledge, by presenting scales to measure authenticity and personal creativity, and by discussing how identity/belongingness may affect student success.