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This study investigates how high school-aged youth engage in algorithm auditing to identify and understand biases in artificial intelligence and machine learning (AI/ML) tools they encounter daily. With AI/ML technologies being increasingly integrated into young people’s lives, there is an urgent need to equip teenagers with AI literacies that build both technical knowledge and awareness of social impacts. Algorithm audits (also called AI audits) have traditionally been employed by experts to assess potential harmful biases, but recent research suggests that non-expert users can also participate productively in auditing. We conducted a two-week participatory design workshop with 14 teenagers (ages 14–15), where they audited the generative AI model behind TikTok’s Effect House, a tool for creating interactive TikTok filters. We present a case study describing how teenagers approached the audit, from deciding what to audit to analyzing data using diverse strategies and communicating their results. Our findings show that participants were engaged and creative throughout the activities, independently raising and exploring new considerations, such as age-related biases, that are uncommon in professional audits. We drew on our expertise in algorithm auditing to triangulate their findings as a way to examine if the workshop supported participants to reach coherent conclusions in their audit. Although the resulting number of changes in race, gender, and age representation uncovered by the teens were slightly different from ours, we reached similar conclusions. This study highlights the potential for auditing to inspire learning activities to foster AI literacies, empower teenagers to critically examine AI systems, and contribute fresh perspectives to the study of algorithmic harms. 

This study investigates how high school-aged youth engage in algorithm auditing to identify and understand biases in artificial intelligence and machine learning (AI/ML) tools they encounter daily. With AI/ML technologies being increasingly integrated into young people’s lives, there is an urgent need to equip teenagers with AI literacies that build both technical knowledge and awareness of social impacts. Algorithm audits (also called AI audits) have traditionally been employed by experts to assess potential harmful biases, but recent research suggests that non-expert users can also participate productively in auditing. We conducted a two-week participatory design workshop with 14 teenagers (ages 14–15), where they audited the generative AI model behind TikTok’s Effect House, a tool for creating interactive TikTok filters. We present a case study describing how teenagers approached the audit, from deciding what to audit to analyzing data using diverse strategies and communicating their results. Our findings show that participants were engaged and creative throughout the activities, independently raising and exploring new considerations, such as age-related biases, that are uncommon in professional audits. We drew on our expertise in algorithm auditing to triangulate their findings as a way to examine if the workshop supported participants to reach coherent conclusions in their audit. Although the resulting number of changes in race, gender, and age representation uncovered by the teens were slightly different from ours, we reached similar conclusions. This study highlights the potential for auditing to inspire learning activities to foster AI literacies, empower teenagers to critically examine AI systems, and contribute fresh perspectives to the study of algorithmic harms. 

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 (i.e., 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. 

While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing—a method for understanding algorithmic systems’ opaque inner workings and external impacts from the outside in. In this paper, we review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study. 

Amongst efforts to realize computer science (CS) for all, recent critiques of racially biased technologies have emerged (e.g., facial recognition software), revealing a need to critically examine the interaction between computing solutions and societal factors. Yet within efforts to introduce K-12 students to such topics, studies examining teachers' learning of critical computing are rare. To understand how teachers learn to integrate societal issues within computing education, we analyzed video of a teacher professional development (PD) session with experienced computing teachers. Highlighting three particular episodes of conversation during PD, our analysis revealed how personal and classroom experiences—from making a sensor-based project to drawing on family and teaching experiences—tethered teachers’ weaving of societal and technical aspects of CS and enabled reflections on their learning and pedagogy. We discuss the need for future PD efforts to build on teachers’ experiences, draw in diverse teacher voices, and develop politicized trust among teachers. 

While making physical computational artifacts such as robots or electronic textiles is growing in popularity in CS education, little is known about student informal conceptions of these systems. To study this, we video-recorded think-aloud sessions (~10 minutes each) of 22 novice CS high school students explaining their understanding of everyday physical computing systems and qualitatively analyzed transcripts and student drawings for their structural, behavioral, and functional understanding of these systems. Most students identified the presence of programs in making these systems functional but struggled to account them structurally and behaviorally. A few students pointed out probable programming constructs in shaping underlying mechanisms, drawing from their prior programming experiences. To integrate these systems in computing education, we call for pedagogical designs to address the invisibility of computation—both of structural interconnections and of program execution. 

Comprehending programs is key to learning programming. Previous studies highlight novices’ naive approaches to comprehend ing the structural, functional, and behavioral aspects of programs. And yet, with the majority of them examining on-screen program ming environments, we barely know about program comprehension within physical computing—a common K-12 programming context. In this study, we qualitatively analyzed think-aloud inter view videos of 22 high school students individually comprehending a given text-based Arduino program while interacting with its corresponding functional physical artifact to answer two questions: 1) How do novices comprehend the given text-based Arduino pro gram? And, 2) What role does the physical artifact play in program comprehension? We found that novices mostly approached the program bottom-up, initially comprehending structural and later functional aspects, along different granularities. The artifact provided two distinct modes of engagement, active and interactive, that supported the program’s structural and functional comprehension. However, behavioral comprehension i.e. understanding program execution leading to the observed outcome was inaccessible to many. Our findings extend program comprehension literature in two ways: (a) it provides one of the very few accounts of high school students’ code comprehension in a physical computing con text, and, (b) it highlights the mediating role of physical artifacts in program comprehension. Further, they point directions for future pedagogical and tool designs within physical computing to better support students’ distributed program comprehension. 

A 1971 memo by Papert and Solomon introduced twenty things to do with a computer which became the foundation for constructionism. In this paper, we propose bringing constructionist activities into making with living materials. Significant developments in tools and methods have turned biology into a design science: it is now possible to make things with biology—or biodesign— rather than just observing processes and behaviours. Our list of twenty things to make with biology includes examples from making colours, toys, games, insulin, batteries, sensors and more. In the discussion, we review how making with biology addresses key affordances of constructionist learning: “tinkerability,” the ability to experiment; “perceptibility,” the immediacy of feedback on learning process; “expressivity,” the personal customization of products; and “usability,” the ability to use learning designs in everyday contexts. We conclude with an overview of accessible and affordable tools available to K-12 education. 

Mindsets play an important role in persevering in computer science: while some learners perceive bugs as opportunities for learning, others become frustrated with failure and see it as a challenge to their abilities. Yet few studies and interventions take into account the motivational and emotional aspects of debugging and how learning environments can actively promote growth mindsets. In this paper, we discuss growth mindset practices that students exhibited in “Debugging by Design,” an intervention created to empower students in debugging—by designing e-textiles projects with bugs for their peers to solve. Drawing on observations of four student groups in a high school classroom over a period of eight hours, we examine the practices students exhibited that demonstrate the development of growth mindset, and the contexts where these practices emerged. We discuss how our design-focused, practice-first approach may be particularly well suited for promoting growth mindset in domains such as computer science.