The recent proliferation of artificial intelligence and machine learning (AI/ML) systems highlights the need for all people to develop effective competencies to interact with and examine AI/ML systems. We study shifts in five experienced high school CS teachers' understanding of AI/ML systems after one year of participatory design, where they co-developed lessons on AI auditing, a systematic method to query AI/ML systems. Drawing on individual and group interviews, we found that teachers' perspectives became more situated, grounding their understanding in everyday contexts; more critical, reflecting growing awareness of harms; and more agentic, highlighting possibilities for action. Further, across all three perspectives, teachers consistently framed algorithmic justice through their role as educators, situating their concerns within their school communities. In the discussion, we consider the ways teachers' perspectives shifted, how AI auditing can shape these shifts, and the implications of these findings on AI literacy for both teachers and students.

Today's youth have extensive experience interacting with artificial intelligence and machine learning applications on popular social media platforms, putting youth in a unique position to examine, evaluate, and even challenge these applications. Algorithm auditing is a promising candidate for connecting youth's everyday practices in using AI applications with more formal scientific literacies (syncretic designs). In this paper, we analyze high school youth participants' everyday algorithm auditing practices when interacting with generative AI filters on TikTok, revealing thorough and extensive examinations, with youth rapidly testing filters with sophisticated camera variations and facial manipulations to identify filter limitations. In the discussion, we address how these findings can provide a foundation for developing designs that bring together everyday and more formal algorithm auditing.

Research on children and youth's participation in different roles in the design of technologies is one of the core contributions in child-computer interaction studies. Building on this work, we situate youth as advisors to a group of high school computer science teacher- and researcher-designers creating learning activities in the context of emerging technologies. Specifically, we explore algorithm auditing as a potential entry point for youth and adults to critically evaluate generative AI algorithmic systems, with the goal of designing classroom lessons. Through a two-hour session where three teenagers (16-18 years) served as advisors, we (1) examine the types of expertise the teens shared and (2) identify back stage design elements that fostered their agency and voice in this advisory role. Our discussion considers opportunities and challenges in situating youth as advisors, providing recommendations for actions that researchers, facilitators, and teachers can take to make this unusual arrangement feasible and productive.

Purpose The purpose of this paper is to examine how a clinical interview protocol with failure artifact scenarios can capture changes in high school students’ explanations of troubleshooting processes in physical computing activities. The authors focus on physical computing, as finding and fixing hardware and software bugs is a highly contextual practice that involves multiple interconnected domains and skills. Design/methodology/approach This paper developed and piloted a “failure artifact scenarios” clinical interview protocol. Youth were presented with buggy physical computing projects over video calls and asked for suggestions on how to fix them without having access to the actual project or its code. Authors applied this clinical interview protocol before and after an eight-week-long physical computing (more specifically, electronic textiles) unit. They analyzed matching pre- and post-interviews from 18 students at four different schools. Findings The findings demonstrate how the protocol can capture change in students’ thinking about troubleshooting by eliciting students’ explanations of specificity of domain knowledge of problems, multimodality of physical computing, iterative testing of failure artifact scenarios and concreteness of troubleshooting and problem-solving processes. Originality/value Beyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, the “failure artifact scenarios” clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students’ abilities over time.

Debugging physical computing projects provides a rich context to understand cross-disciplinary problem solving that integrates multiple domains of computing and engineering. Yet understanding and assessing students' learning of debugging remains a challenge, particularly in understudied areas such as physical computing, since finding and fixing hardware and software bugs is a deeply contextual practice. In this paper we draw on the rich history of clinical interviews to develop and pilot "failure artifact scenarios" in order to study changes in students' approaches to debugging and troubleshooting electronic textiles (e-textiles). We applied this clinical interview protocol before and after an eight-week-long e-textiles unit. We analyzed pre/post clinical interviews from 18 students at four different schools. The analysis revealed that students improved in identifying bugs with greater specificity, and across domains, and in considering multiple causes for bugs. We discuss implications for developing tools to assess students' debugging abilities through contextualized debugging scenarios in physical computing.

Background and ContextWhile debugging is recognized as an essential practice, for many students, encountering bugs can generate emotional responses such as fear and anxiety that can lead to disengagement and the avoidance of computer programming. Growth mindsets can support perseverance and learning in these situations, yet few studies have investigated how growth mindsets emerge in practice amongst K-12 computing students facing physical computing debugging challenges.ObjectiveWe seek to understand what (if any) growth mindset practices high school students exhibited when creating and exchanging buggy physical computing projects for their peers to solve during a Debugging by Design activity as part of their introductory computing course.MethodWe focused on moment-to-moment microgenetic analysis of student interactions in designing and solving bugs for others to examine the practices students exhibited that demonstrated the development of a growth mindset and the contexts in which these practices emerged.FindingsWe identified five emergent growth mindset practices: choosing challenges that lead to more learning, persisting after setbacks, giving and valuing praise for effort, approaching learning as constant improvement, and developing comfort with failure. Students most often exhibited these practices in peer-to-peer interactions and while making buggy physical computing projects for their peers to solve.ImplicationsOur analysis contributes to a more holistic understanding of students' social, emotional, and motivational approaches to debugging physical computing projects through the characterization of growth mindset practices. The presented inventory of growth mindset practices may be helpful to further study growth mindset in action in other computing settings.

Background and Context: Debugging is a challenging yet understudied practice within recent collaborative K-12 physical computing contexts. We examined think-aloud interviews and reflections of seven high school student pairs who debugged researcher-designed buggy electronic textile projects. Objective: We asked: (1) What strategies did student pairs employ as they debugged e-textile projects? (2) How did interactions between people, tools, and representations shape debugging approaches? (3) How did students reflect on this debugging experience in relation to their learning in the e-textile unit? Method: We qualitatively analyzed eight think-aloud videos (similar to 45 min each) and student reflections (similar to 15 min twice). Findings: Debugging required iteratively problem-solving across multiple modes, employing system-level , and a mix of collaborative and individual strategies. All students attributed various gains to this activity, including problem-solving-related practices and positive stances toward collaboration. Implications: Our analysis expands debugging research to include K-12 physical computing contextand highlights the potential of similar debugging activities as intentional learning experiences.

Debugging (or troubleshooting) provides a rich context to foster problem-solving. Yet, while we know much about some problems and strategies that novices face in programming on-screen, we know far less about debugging and troubleshooting in the context of physical computing, where coding issues may overlap with materially embedded problems. In this paper, we study the thought processes novice students employed and the challenges they faced in debugging an electronic textile project with multiple overlapping problems that crossed physical, electronic, and computational domains. We employed a think-aloud protocol to develop an instrumental case study by video-recording 45 minutes of one pair of 9th-grade students debugging and fixing a buggy e-textile project. The problem space included the computational system's programmatic, electronic, and physical spatial aspects, which are more generally reflective of physical computing systems. We found that (1) students' troubleshooting was more recursive and less linear than traditional approaches that usually propose linear, procedural, step-wise activities, and (2) students coordinated their approach across multiple modalities, taking advantage of a distributed set of tools and people in order to tackle a complex set of problems. In the discussion, we address various pedagogical implications for improving teaching about troubleshooting.

Background and Context: Few instruments exist to measure students' CS engagement and learning especially in areas where coding happens with creative, project-based learning and in regard to students' self-beliefs about computing. Objective: We introduce the CS Interests and Beliefs Inventory (CSIBI), an instrument designed for novice secondary students learning by designing projects (particularly with physical computing). The inventory contains subscales on beliefs on problem solving competency, fascination in design, value of CS, creative expression, and beliefs about context-specific CS abilities alongside programming mindsets and outcomes. We explain the creation of the instrument and attend to the role of mindsets as mediators of self-beliefs and how CSIBI may be adapted to other K-12 project-based learning settings. Method: We administered the instrument to 303 novice CS secondary students who largely came from historically marginalized backgrounds (gender, ethnicity, and socioeconomic status). We assessed the nine-factor structure for the 32-item instrument using confirmatory factor analysis and tested the hypothesized model of mindsets as mediators with structural equation modeling. Findings: We confirmed the nine factor structure of CSIBI and found significant positive correlations across factors. The structural model results showed that problem solving competency beliefs and CS creative expression promoted programming growth mindset, which subsequently fostered students' programming self-concept. Implications: We validated an instrument to measure secondary students' self-beliefs in CS that fills several gaps in K-12 CS measurement tools by focusing on contexts of learning by designing. CSIBI can be easily adapted to other learning by designing computing education contexts.

Even as efforts to promote K-12 CS education forge ahead, there is a growing consensus that students must also be taught artificial intelligence (AI) and machine learning (ML) in order to be prepared for the fast-changing world powered by AI/ML. How can ensure that we leverage learnings from two decades of CS education research and practice, and build on successes while mitigating missteps? This panel invites researchers with deep expertise in 'CSForAll' efforts for a timely discussion and sharing of valuable lessons from CS education efforts about pedagogies, attention to equity, and teacher preparation that will also benefit K-12 AI education.

Establishing what constitutes creativity in a domain is something for which we often look to experts-individuals versed in a domain's history and able to identify timeworn ideas from fresh ones. Such valuations of creative merit are tied to a familiarity with past and present trends and, therefore, opinions of newcomers are often ignored. However, what about domains that build upon new, unexplored practices? This study examines the creativity ratings of judges with varying expertise in the emergent domain of electronic textiles (or e-textiles). E-textiles are fabrics that have programmable electronics such as sensors and actuators embedded in them toward a variety of expressive and functional ends. Judges included domain pioneers ("experts"), individuals with over 20 hr of nonprofessional experience in the domain ("quasi-experts"), and individuals untrained in the domain ("novices"). Each group evaluated the creativity of e-textile artifacts from an online gallery using the Consensual Assessment Technique (CAT). Our analyses found high interjudge reliability within all groups and between quasi-experts and experts, suggesting that quasi-experts could be sufficiently trained to judge the creativity of artifacts on par with experts. Furthermore, larger panels of novice judges may serve as an alternative, but it would be with the caveat that novice scores represent the opinions of general audiences that might not understand technical practices of e-textiles. Findings offer alternative considerations for how creativity is assessed in emergent, technology-rich domains and have implications for judge recruitment.

This longitudinal case study of a Thai farmer shares a remarkable 15+ year-long constructionist learning journey. Through close mentorship and projects grounded in agricultural village life, this farmer transformed his life in radical ways, developing planning tools and thinking processes that enabled him and his family to work their way out of crushing debt and becoming an expert in his community. We focus on the representational skills he developed in mapping, mathematics, and data visualization. This adult learner's story emphasizes the critical role of representational tools as “technologies” of cognition and speaks to the broad applicability of constructionism to change how learners of all ages perceive, organize, and relate to their worlds.

Learning sciences researchers, in correspondence with the increasing usage of design-based research, increasingly tend to implement similar theories, curricula, tools, and even learners across different contexts like schools and out-of-school settings.Successfully navigating these movements and transitions involves understanding the dynamic relationship between our (researchers') imagined design's learning goals, and the goals and constraints of learners, educators, and other stakeholders mediating any implementation.In this symposium, we present a diverse breadth of work which highlight different design, analytic, and theoretical lenses to examine these transitions.This symposium aims to encourage researchers to consider such extensions of their work, and surface grammars of analyses that can help researchers involved in such work, through shared discussion across five different projects implementing and analyzing their different implementations in contrasting ways.

Debugging, finding and fixing bugs in code, is a heterogeneous process that shapes novice learners' self-beliefs and motivation in computing. Our Debugging by Design intervention (DbD) provocatively puts students in control over bugs by having them collaborate on designing creative buggy projects during an electronic textiles unit in an introductory computing course. We implemented DbD virtually in eight classrooms with two teachers in public schools with historically marginalized populations, using a quasi-experimental design. Data from this study included post-activity results from a validated survey instrument (N=144). For all students, project completion correlated with increased computer science creative expression and e-textiles coding self-efficacy. In the comparison classes, project completion correlated with reduced programming anxiety, problem-solving competency beliefs, and programming self-concept. In DbD classes, project completion is uniquely correlated with increased fascination with design and programming growth mindset. In the discussion, we consider the relative benefits of DbD versus other open-ended projects.

While the last two decades have seen an increased interest in STEAM (science, technology, engineering, arts, and mathematics) in K-12 schools, few efforts have focused on the teachers and teaching practices necessary to support these interventions. Even fewer have considered the important work that teachers carry out not just inside classrooms but beyond the classroom walls to sustain such STEAM implementation efforts, from interacting with administrators to recruiting students and persuading parents about the importance of arts and computer science. In order to understand teachers’ needs and practices regarding STEAM implementation, in this paper, we focus on eight experienced computer science teachers’ reflections on implementing a STEAM unit using electronic textiles, which combine crafting, circuit design, and coding so as to make wearable artifacts. We use a broad lens to examine the practices high school teachers employed not only in their classrooms but also in their schools and communities to keep these equitable learning opportunities going, from communicating with other teachers and admins to building a computer science (CS) teacher community across district and state lines. We also analyzed these reflections to understand teachers’ own social and emotional needs—needs important to staying in the field of CS education—better, as they are relevant to engaging with learning new content, applying new pedagogical skills, and obtaining materials and endorsements from their organizations to bring STEAM into their classrooms. In the discussion, we contemplate what teachers’ reported practices and needs say about supporting and sustaining equitable STEAM in classrooms.