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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. 

Modeling is a cornerstone of professional scientific practice, however, there aren’t enough opportunities for youth to leverage their own perspectives when engaging in modeling inquiry. This paper describes three design dimensions—interdisciplinarity, intermodality, & intergenerationality--of a 2-week long summer camp that leveraged theories of syncretism to integrate dance, science and computing in order to support youth contributions in modeling practices. The camp engaged 12 middle school youth, 2 scientists and 3 choreographers in adopting a complex systems lens and engaging in collaborative inquiry around the scientists’ research systems using choreographic and digital NetLogo modeling. Using discourse, video stills, and narrative description of a group that modeled spinal cord injuries, we show how these three dimensions disrupted barriers between disciplines (science & dance), modes of sense- making (movement & computation), and inequitable power dynamics (youth and adults). In the discussion, we draw out contributions to the literature particularly on scientific modeling. 

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. 

There are strong motivations to implement integrated STEAM activities that engage with key ideas in mathematics. In integrating mathematics with other STEM disciplines, however, epistemic tensions can emerge. Rather than attempting to suppress, avoid, or adjudicate these tensions, we propose a strategy of “epistemic rekeying,” in which epistemic tensions are offered as provocations for students to create playful and artistic responses. This approach takes epistemic tensions seriously and makes them accessible to young learners. We give the rationale for this approach and describe settings where students’ creativity suggested its potential to us. 

STEM integration holds significant promise for supporting students in making connections among ideas and ways of thinking that might otherwise remain “siloed.” Nevertheless, activities that integrate disciplines can present challenges to learners. In particular, they can require students to shift epistemological framing, demands that can be overlooked by designers and facilitators. We analyze how students in an 8th grade mathematics classroom reasoned about circles, across math and coding activities. One student showed evidence of shifting fluently between different frames as facilitators had expected. The dramatic change in his contributions gauge the demands of the activities, as do the contributions of other students, who appeared to work within different frames. Our findings have relevance for the design and facilitation of integrated STEM learning environments to support students in navigating such frame-shifts. 

Agent-based modelling (ABM) is a powerful approach for simulating complexity and for understanding the emergent phenomena core to multiple disciplines across the physical and social sciences (Wilensky, 2001). ABM is thus often understood as an innovation in STEM education, providing a representational infrastructure for understanding complexity by “growing it” (Epstein & Axtell, 1996; Wilensky & Papert, 2010). While this is certainly true, we argue that expressive and artistic uses of “swarms” of computational agents can also provide accessible entry points for learners and can support them in developing a range of intuitions about the kinds of phenomena that they might simulated with ABM. This offers a “STEAM” oriented introduction to modelling, connecting artistic perspectives with scientific perspectives in fundamental ways. In this paper we describe the iterative design and implementation of activities that highlight the expressive potential and social syntonicity (Brady et al, 2016) of one of the fundamental types of agent in the ABM toolkit (the “patches”). We describe a setting in which we have done design-based research over two years, in summer camps (entitled “Code Your Art”) and school-year activities involving rising fifth through eighth grade students (participants aged from 10-15) attending school in a mid-sized urban district in the southeastern USA with a high proportion of traditionally underserved and minoritized youth. 

This paper reports on ethnographic work that explores professional dancers’ practice, “physical research,” as a members’ phenomenon. The practice of physical research enables this company to explore their unknown expressive potential as an ensemble through an iterative process of creating and refining collective, full-body movements. As a case of ensemble learning that foregrounds artistic agency, physical research supports a community of learners (1) to be comfortable and open with “unknowing,” (2) to engage playfully and “absurdly” in their inquiry and (3) to persist collectively in their inquiry over long stretches of time. These three aspects, each difficult to foster and lauded in learning environments, are grounded in the creative and physical nature of physical research. Here and in ongoing work, I argue that this practice can inspire the design of formal and informal learning environments.