Artificial intelligence (AI) systems are increasingly prevalent in youths’ lives, despite documentation and concern of potential algorithmic harm. With responsible AI (RAI) efforts aiming to address harms, youth are largely overlooked as contributors, despite being stakeholders of AI. This paper explores perspectives on challenges, barriers, and opportunities of youth participation in responsibly creating AI. Through workshops with 16 teens and 11 parents, as well as interviews with 8 AI practitioners, we find that while all groups recognize the value of youth perspectives, the opinions of those not directly paying for AI are not prioritized. Many parents were optimistic about their youths’ ability to contribute, while youth showed a desire to contribute, coupled with an awareness of their strengths and limitations. Practitioners saw the potential of youth to contribute but not necessarily as empowered decision makers in RAI processes. We offer insights on involving youth in RAI, balancing protection with agency.

Youths’ critical evaluation of AI is key in supporting their informed interactions with AI systems. We report on a five-day AI literacy camp with teens (N = 16), where they used a custom text-to-image interface to systematically evaluate GenAI. We examined: (1) how youth defined “goodness” in AI behavior, (2) applied (or did not) their ideas of goodness to AI evaluation, and (3) challenges that emerged when supporting systematic evaluation. Youth defined goodness as prompt adherence, realism, and representation. However, in their evaluations, they often conflated personal preference (e.g., personally enjoyable content) with output quality and noticed surface-level patterns (sometimes without deeper causal interpretation). Structured comparisons and peer sensemaking helped youth produce more specific claims. We conclude by reflecting on challenges and opportunities and suggest design implications.

Responsible AI (RAI) practices are increasingly important for practitioners in anticipating and addressing potential harms of AI, and emerging research suggests that AI practitioners often learn about RAI on-the-job.More generally, learning at work is social; thus, this work explores the interpersonal aspects of learning about RAI on-the-job.Through workshops with 21 industry-based RAI educators, we offer the first empirical investigation into interpersonal processes and dimensions of learning about RAI at work.This study finds key phases of RAI are sites for ongoing interpersonal learning, such as critical reflection about potential RAI impacts and collective sense-making about operationalizing RAI principles.We uncover a significant gap between these interpersonal learning processes and current approaches to learning about RAI.Finally, we identify barriers and supports for interpersonal learning about RAI.We close by discussing opportunities to better enable interpersonal learning about RAI on-the-job and the broader implications of interpersonal learning for RAI.

Artificial intelligence (AI) is ubiquitous in K-12 youths' everyday lives. However, it has become increasingly well-documented that AI can cause harm by reflecting and amplifying societal biases. While many youth are not currently empowered to engage in broader responsible AI discourse and processes, there is great potential. Foundational to engaging in critical conversations is ability to critique AI. We present the RAD framework, designed to scaffold critique of AI in three steps: Recognize (harms of AI), Analyze (societal aspects of AI harms), and Deliberate (what more responsible AI could be). We ran a workshop study with racially diverse middle school girls (N = 21) to investigate its effectiveness. We found that through being scaffolded with the framework, the youth could articulate biases that they saw in an AI scenario and consider how biases may impact different stakeholders. They then could contemplate how different stakeholders had varying amounts of power in the AI scenario and what that meant in terms of creating more responsible AI systems and processes. After participating in the study, the youth felt more strongly about voicing their opinions about AI with others. The RAD framework and activities work toward emboldening youths' engagement in critical discourse about AI.

The importance of considering local context and partnering with target users is well established in co-design. Less common is an examination of the adaptations needed when deploying the same co-design program across heterogenous settings to maximize program efficacy and equity. We report on our experience co-designing educational games with six culturally and socioeconomically diverse afterschool sites over two years, and insights from interviewing ten program administrators across all sites. We found that even within the same afterschool program network, site differences in organizational culture and resources impacted the effectiveness of co-design programs, the co-design output, and expectations for student engagement. We characterize our afterschool partners into different archetypes – Safe Havens, Recreation Centers, Homework Helpers, and STEM Enrichment Centers. We provide recommendations for conducting co-design at each archetype and reflect on strategies for increasing equitable partnerships between researchers and afterschool centers.

Robot technologies have been introduced to computing education to engage learners. This study introduces the concept of co-creation with a robot agent into culturally-responsive computing (CRC). Co-creation with computer agents has previously focused on creating external artifacts. Our work differs by making the robot agent itself the co-created product. Through participatory design activities, we positioned adolescent girls and an agentic social robot as co-creators of the robot’s identity. Taking a thematic analysis approach, we examined how girls embody the role of creator and co-creator in this space. We identified themes surrounding who has the power to make decisions, what decisions are made, and how to maintain social relationship. Our findings suggest that co-creation with robot technology is a promising implementation vehicle for realizing CRC.

Artificial intelligence (AI) literacy is especially important for those who may not be well-represented in technology design. We worked with ten Black girls in fifth and sixth grade from a predominantly Black school to understand their perceptions around fair and accountable AI and how they can have an empowered role in the creation of AI. Thematic analysis of discussions and activity artifacts from a summer camp and after-school session revealed a number of findings around how Black girls: perceive AI, primarily consider fairness as niceness and equality (but may need support considering other notions, such as equity), consider accountability, and envision a just future. We also discuss how the learners can be positioned as decision-making designers in creating AI technology, as well as how AI literacy learning experiences can be empowering.

Families and youth often interact with artificial intelligence (AI) and are stakeholders in technology. However, while AI can enhance interactions with technology, it is well-known that AI can also be biased and cause harm. Therefore, AI literacy is important, such that users understand both some technical components (e.g., training data, what is and is not AI and why), as well as ethical aspects (e.g., bias in society reflected in AI-powered technology) in order to make informed and empowered decisions about interacting with technologies. AI learning opportunities are scarce, and few leverage informal intergenerational learning as an opportunity for youth to engage with topics in AI and ethics. Games are an underexplored yet promising route for intergenerational play and learning for families and youth without much prior exposure to computing or AI. This talk highlights how and why games are particularly fitting to support families in gaining AI literacy through (1) games as an educational tool to build stronger mental models of AI, (2) intergenerational play as a way to scaffold adults and children in having a dialogue about more complex ethical considerations of AI, and (3) position families in roles of influence, where they may be empowered as techno-social change agents. We believe that games can help fill a gap by making AI literacy, an important family matter, accessible to a range of adult and youth learners.

Children interact frequently with artificial intelligence (AI) in their everyday lives. Their understanding of AI technology, however, is often very limited. AI literacy is vital to make informed and empowered decisions about engaging with technology. The ability to imagine future technology designs and applications is a core competency of AI literacy, but few studies have used booklets, a well-studied design fiction tool, as a technique with children in imagining future AI. We explored how a booklet-based design fiction method can support AI literacy with fifth graders (N = 7) from marginalized backgrounds. We describe two benefits of using booklets to create AI design fiction with children in our educational workshop: (1) grounding abstract AI concepts, (2) prolong post session dialogues. This work contributes a new type of scaffolded activity that supports youth learners in AI education.

Youth regularly use technology driven by artificial intelligence (AI). However, it is increasingly well-known that AI can cause harm on small and large scales, especially for those underrepresented in tech fields. Recently, users have played active roles in surfacing and mitigating harm from algorithmic bias. Despite being frequent users of AI, youth have been under-explored as potential contributors and stakeholders to the future of AI. We consider three notions that may be at the root of youth facing barriers to playing an active role in responsible AI, which are youth (1) cannot understand the technical aspects of AI, (2) cannot understand the ethical issues around AI, and (3) need protection from serious topics related to bias and injustice. In this study, we worked with youth (N = 30) in first through twelfth grade and parents (N = 6) to explore how youth can be part of identifying algorithmic bias and designing future systems to address problematic technology behavior. We found that youth are capable of identifying and articulating algorithmic bias, often in great detail. Participants suggested different ways users could give feedback for AI that reflects their values of diversity and inclusion. Youth who may have less experience with computing or exposure to societal structures can be supported by peers or adults with more of this knowledge, leading to critical conversations about fairer AI. This work illustrates youths' insights, suggesting that they should be integrated in building a future of responsible AI.

Creating technology products using codesign techniques often results in higher end-user engagement compared to expert-driven designs. Codesign sessions are typically structured in flexible and informal ways to achieve equal design partnerships, especially in adult-child interactions. This generally leads to better design output, however, it may also increase the enactment of socially constructed stereotypes and biases in ways that negatively affect the experiences of racial minorities and girls/women in design spaces. We codesigned a video game with a K-5 afterschool program located in a working-class, rural, predominantly white county over 20 weeks. We uncover ways that the codesign process and different activity types can create a permissive environment for enacting behaviors that are harmful to minorities. We discuss ways to manage and restructure codesign programs to be more conducive for children and adults from diverse backgrounds, ultimately leading to healthier design partnerships.

Youth regularly interface with AI technology that leverages supervised machine learning. However, it is well-known that biased training data can result in harmful algorithmic bias. Thus, it is important that youth and families understand training data in machine learning. We present Data Detectives, a child-friendly tabletop card game about training data. Based on three research-based design principles: low-stakes experimentation to support curiosity, games facilitating conversation, and tangible and embodied learning for abstract concepts, the game supports learning the high-level mechanics of training data in supervised machine learning, as well as practicing critical discussion of training data related to algorithmic bias. Contributing to AI literacy opportunities, this game aims to facilitate playful peer-peer and child-parent learning.

Computing education is important for K-12 learners, but not all learners resonate with common educational practices. Culturally responsive computing initiatives center and empower learners from diverse and historically excluded backgrounds. Recently, a number of educational programs have been developed and curated for an online experience. In this paper, we describe an online synchronous culturally responsive computing (CRC) camp for middle school girls (ages 11-14 years old) and report on challenges and successes from running the camp curriculum four times over the course of a year. We also describe core iterative changes we made between our runs. We then discuss lessons learned related to building rapport and connection among learners, centering learners of different backgrounds in an online synchronous environment, and facilitating reflection on power and identity aimed at positioning learners as techno-social change agents. Lastly, we offer recommendations for running online CRC experiences.

Computer science (CS) education is an important subject for K-12 students in an increasingly computational world. However, common CS education practices may not be inclusive of all learners. Culturally responsive computing (CRC) initiatives aim to center and empower learners from diverse and historically excluded backgrounds. With a sudden shift to online learning, virtual educational experiences have been developed. We describe three main findings from running three iterations of an online synchronous CRC camp for middle school girls, which are: (1) Integration of power, identity, and CS concepts, (2) Participation in CS vs. power and identity activities based on learners' backgrounds, and (3) Adjusting instructor expectations about learner engagement to be more open-ended.

As schools begin offering more opportunities for computer science education, it is important to consider the viewpoints of all stakeholders. Parents, in particular, can have an outsize impact on their children's educational experience and attitudes towards STEM topics, but few studies investigate parent perceptions of CS, beliefs around whether and why learning CS is important for their children, and preferences for tools to learn programming. We ran a survey with 133 parent respondents of children in 6th to 12th grade to investigate perspectives on computing and CS education. Most parents viewed CS as important for their children to learn; however, fewer actively encouraged their child to engage with CS, and those who did tended to have higher prior knowledge in CS. In considering three types of educational programming interfaces (block, text, and hybrid), parents of varying experience levels preferred the hybrid tool, commonly because they believed that it could provide the best educational value. This work contributes to understanding parent stances and preferences on CS education, as they play an important role in supporting and facilitating their children's access to and experience in CS learning opportunities.

Culturally responsive STEM and computing initiatives aim to engage and embolden a diverse range of learners, center their identity and experiences in curriculum, and connect learners to each other and their communities. With an abrupt pivot to online learning at the beginning of 2020, more educational experiences have taken place virtually. We ran a virtual synchronous culturally responsive computing camp and saw that establishing the right environment online to support a good sense of connectedness was challenging. To investigate this further, we interviewed eight K-12 instructors of culturally responsive STEM and computing programs. Three themes emerged on defining and cultivating connectedness in learning experiences, the role of equity in supporting community online, and affordances of being online specific to culturally responsive perspectives. We support our thematic findings with vignettes from the camp data. In this study, we address K-12 culturally responsive STEM and computing instructors' beliefs, experiences, and approaches regarding cultivating connectedness online. This work fills a gap in understanding instructor perspectives on building in-program and broader community connections online from a culturally responsive STEM and computing lens.

This paper investigates in-class interactions in synchronous online classrooms when the choice of modality is discretionary, such that students choose when and if they turn on their cameras and microphones. Instructor interviews (N = 7) revealed that most students preferred not to share videos and verbally participate. This hindered instructors’ ability to read their classrooms and make deeper connections with students. Survey results (N = 102) suggested that students felt a lacking sense of community in online vs. in-person lectures. Some students felt uncomfortable broadcasting their appearances to everyone in the class, and some were unaware of the benefits for instructors. Most students favored using the text chat to participate. Considering the needs of both instructors and students, we propose recommendations to mitigate the loss of classroom interactions by collecting and presenting less invasive social cues in an aggregated format, and incorporating opportunities for informal exchanges and individual control to spark peer bonding.

Dialog with a social pedagogical robot or agent is a powerful way for kids to learn [1, 5] but may limit the formation of an agentic relationship with the technology [9]. One main purpose of conversational agents is to allow the user to have a natural interaction that reduces the need to learn artificial conventions [6], but dialog systems fall short with respect to failure recovery, vocabulary diversity, remembering conversational history, and other measures [2, 3]. Further, Hill et. al. [4] found that people adapt their model of communication to match a chatbot’s in the same way they do with a child or non-native speaker. Thus, users conversing with a pedagogical agent are implicitly trained to shape their behavior to suit the technology rather than shaping the technology. For young learners, particularly among populations that have been historically excluded from technology fields, this limits agency and reinforces marginalizing power structures [9].

In online asynchronous learning environments, students are assigned exercises, but it is not clear how to incorporate the kinds of actions an in-person tutor might take such as explaining, providing more practice, prompting for reflection, and motivating. We explore approaches to adding "Drop-Downs'' that appear after a student submits an answer and that contain additional information to support learning. We conducted randomized A/B experiments exploring the impact of these Drop-Downs on student learning in the online portion of a flipped CS1 course. The deployed Drop-Downs in this course provided explanations, reflective prompts, additional problems, and motivational messages. The results suggest that students benefit from various Drop-Downs in different contexts, indicating the possibility of personalizing content based on the student's state. We discuss the resulting design implications of Drop-Downs in online learning systems.

Engaged preparation and study in combination with lectures are important for all courses but are particularly critical for online, hybrid, and inverted classrooms. Many instructors use online systems to deliver new course content and exercises, but students often delay assignments or game these systems (e.g., guessing on multiple-choice questions), often to the detriment of their learning. In an inverted CS1 course, many students self-reported high rates of gaming-the-system behavior, so we examine survey data to identify factors that contribute to engagement in these maladaptive behaviours. We supplement that analysis with interview data to gain a deeper understanding of the situation. We also implemented and evaluated a previously reported online intervention aimed at reducing gaming behavior. Unlike prior work, our intervention did not have a significant effect on guessing behavior. We discuss why the factors we identified might explain this result, as well as suggest future work to improve our understanding of gaming behaviours and inform the design of systems that encourage effective learning.

Students working on programming homework do not receive the same level of support as in the classroom, relying primarily on automated feedback from test cases. One low-effort way to provide more support is by prompting students to compare their solution to an instructor's solution, but it is unclear the best way to design such prompts to support learning. We designed and deployed a randomized controlled trial during online programming homework, where we provided students with an instructor's solution, and randomized whether they were prompted to compare their solution to the instructor's, to fill in the blanks for a written explanation of the instructor's solution, to do both, or neither. Our results suggest that these prompts can effectively engage students in reflecting on instructor solutions, although the results point to design trade-offs between the amount of effort that different prompts require from students and instructors, and their relative impact on learning.

In the context of online programming homework for a university course, we explore the extent to which learners engage with optional prompts to self -explain answers they choose for problems. Such prompts are known to benefit learning in laboratory and classroom settings [4], but there are less data about the extent to which students engage with them when they are optional additions to online homework. We report data from a deployment of self-explanation prompts in online programming homework, providing insight into how the frequency of writing explanations is correlated with different variables, such as how early students start homework, whether they got a problem correct, and how proficient they are in the language of instruction. We also report suggestive results from a randomized experiment comparing several methods for increasing the rate at which people write explanations, such as including more than one kind of prompt. These findings provide insight into promising dimensions to explore in understanding how real students may engage with prompts to explain answers.