As AI integrates into design practice, designers increasingly use generative AI tools to envision AI-enabled solutions, positioning AI as both design tool and design material. This dual role creates recursive value tensions distinct from traditional design work. We engaged 18 designers in a concept envisioning activity and interviews to understand how they navigate values and recognize potential harms in this context. Our analysis reveals that (i) designers engage in reciprocal reflection-in-action with AI; (ii) this process surfaces multi-level value tensions across tool, designer, and concept; (iii) designers demonstrate greater attunement to harm recognition as a primary design signal than to articulating positive value fulfillment; and (iv) designers exercise anticipatory judgment through meta-design reasoning about how tool assumptions risk propagating into designed concepts and future use contexts. We extend Schön’s reflection-in-action framework and discuss implications for redesigning AI-mediated design tools, supporting harm-centered reasoning, and positioning design as foundational to AI development.

Generative AI (genAI) is increasingly being integrated into children’s everyday lives, not only through screens but also through so-called “screen-free” AI toys. These toys can simulate emotions, personalize responses, and recall prior interactions, creating the illusion of an ongoing social connection. Such capabilities raise important questions about how children understand boundaries, agency, and relationships when interacting with AI toys. To investigate this, we conducted two participatory design sessions with eight children ages 6 - 11 where they engaged with three different AI toys, shifting between play, experimentation, and reflection. Our findings reveal that children approached AI toys with genuine curiosity, profiling them as social beings. However, frequent interaction breakdowns and mismatches between apparent intelligence and toy-like form disrupted expectations around play and led to adversarial play. We conclude with implications and design provocations to navigate children’s encounters with AI toys in more transparent, developmentally appropriate, and responsible ways.

Two of the most socially consequential issues facing today’s children are the rise of artificial intelligence (AI) and the rapid changes to the earth’s climate. Both issues are complex and contested, and they are linked through the notable environmental costs of AI use. Using a systems thinking framework, we developed an interactive system called Ecoprompt to help children reason about the environmental impact of AI. EcoPrompt combines a prompt-level environmental footprint calculator with a simulation game that challenges players to reason about the impact of AI use on natural resources that the player manages. We evaluated the system through two participatory design sessions with 16 children ages 6–12. Our findings surfaced children’s perspectives on societal and environmental tradeoffs of AI use, as well as their sense of agency and responsibility. Taken together, these findings suggest opportunities for broadening AI literacy to include systems-level reasoning about AI’s environmental impact.

This participatory study utilizes the IDC 2026 Research & Design Challenge on sustainability to explore how middle school youth, with varied backgrounds and experiences with emerging technologies, develop maker identities and responsible technology design skills through a series of co-design workshops. We engaged a team of three youth participants and four adult researchers, to develop Milfoil Milly, an AI-powered robotic fish system for detecting invasive milfoil. The process of developing Milfoil Milly through co-design offered an opportunity to investigated (Q1) how designing technology solutions shapes youth identity as technology creators, (Q2) their conceptualizations of sustainability in design solutions, (Q3) their consideration of responsible technology development, and (Q4) how the co-design process impacts both the learning and design experience for youth.

Children today encounter social issues—climate change, conflict, inequality—through digital technologies, and the design of that encounter shapes whether young people move toward lasting civic engagement or toward anxiety and withdrawal. Much of the content children see is optimized for attention through fear and urgency, with few pathways toward meaningful action—contributing to rising distress and disengagement among young people who care deeply but feel powerless to act. This half-day workshop introduces “sustainable care” as a design lens, asking how technology might support children’s sustained engagement with social causes without contributing to empathic distress or burnout. We invite researchers and practitioners across child-computer interaction, games, education, and youth mental health to map this landscape together and develop a research agenda for the CCI community.

AI technologies have become increasingly embedded in children’s lives. While there are many frameworks for designing AI technologies for children’s well-being, few have been tested against the tensions children experience when using AI across different global contexts. This workshop explores how to design AI technologies that enhance children’s digital well-being in accordance with various developmental and child-computer interaction frameworks, as well as what developers and designers should consider as they do so. Our goal is to convene an interdisciplinary international community to pave the way for future research and practice that seeks to design AI technologies with children’s well-being in mind.

Challenges such as hallucinations, biased outputs, and deepfakes underscore the need for AI literacy that helps users question, verify, and make sense of AI outputs. Furthermore, AI literacy in early childhood education (ages 3-8) remains an underdeveloped research area, compared to the rapidly expanding body of work for adults and older students. Yet significant challenges remain, including limited AI knowledge among caregivers and educators, a lack of validated age-appropriate curricula, and ongoing concerns about overuse, privacy abuse, security risks, anthropomorphism, and misunderstandings of AI capabilities. This workshop brings together researchers, educators, and designers to envision what community-centered AI literacy might look like.

As generative AI (genAI) rapidly enters classrooms, accompanied by district-level policy rollouts and industry-led teacher trainings, it is important to rethink the canonical “adopt and train” playbook. Decades of educational technology research show that tools promising personalization and access often deepen inequities due to uneven resources, training, and institutional support. Against this backdrop, we conducted semi-structured interviews with 22 teachers from a large U.S. school district that was an early adopter of genAI. Our findings reveal the motivations driving adoption, the factors underlying resistance, and the boundaries teachers negotiate to align genAI use with their values. We further contribute by unpacking the sociotechnical dynamics—including district policies, professional norms, and relational commitments—that shape how teachers navigate the promises and risks of these tools.

As Artificial Intelligence (AI) becomes increasingly embedded in children's everyday lives, the need to foster AI literacy from an early age has become more urgent.My dissertation will investigate how we can support children in engaging with AI not just as passive users, but as: 1) interpreters who make sense of AI's decisionmaking; 2) resistors who critically examine biased or flawed outputs; and 3) collaborators who co-create with AI in ways that reflect their identities, values, and lived experiences.To date, I have primarily explored the roles of interpreter and resistor through the co-design of interactive systems with children at KidsTeam UW, as well as through classroom deployments and field studies.This work has examined how children make sense of AI's decisions and how they critically respond when those decisions reflect bias or exclusion.Moving forward, I aim to investigate how AI can serve as a meaningful collaborator in supporting children's learning, development, and wellbeing, particularly in educational and therapeutic contexts.

As generative artificial intelligence (genAI) increasingly mediates how children learn, communicate, and engage with digital content, understanding children's hopes and fears about this emerging technology is crucial.In a pilot study with 37 fifth-graders, we explored how children (ages 9-10) envision genAI and the roles they believe it should play in their daily life.Our findings reveal three key ways children envision genAI: as a companion providing guidance, a collaborator working alongside them, and a task automator that offloads responsibilities.However, alongside these hopeful views, children expressed fears about overreliance, particularly in academic settings, linking it to fears of diminished learning, disciplinary consequences, and long-term failure.This study highlights the need for child-centric AI design that balances these tensions, empowering children with the skills to critically engage with and navigate their evolving relationships with digital technologies.

AI technologies, despite having well-documented biases and shortcomings, are becoming increasingly pervasive across various aspects of society. AI biases often reflect and interact with broader societal biases, underscoring the need to support children in understanding these biases so that they can identify when they (or others) are being discriminated against by an AI-based system. To explore this learning through a new methodology, we built an interactive system called CLIP4KIDS. We conducted four classroom sessions with 28 fifth graders in the United States and examined our data using qualitative thematic analysis. Students frequently described AI biases in terms of “assumptions” and “stereotypes” and drew connections between historical injustices and present biases in AI models. This work contributes a novel tool for learning about AI biases, an empirical account of children’s experiences, and a theoretical analysis incorporating Vossoughi and Gutiérrez’s framework of critical pedagogy and sociocultural theory.

The surge in access to and awareness of Generative Artificial Intelligence (GenAI) such as ChatGPT has sparked discussion over the necessary technological literacies and competencies needed to effectively engage with these systems. In this context, we explore AI as a tool that mediates cultural understanding and remediates human values – that are often influenced by biases and inequities. Using participatory design for learning with a group of 13 children (ages 8-13), we engaged in five co-design sessions featuring different modalities for socio-cultural approaches to AI literacy. We found that children were more aware of the cultural mediation aspect of AI when the content of the interaction aligned with their cultural background and context. This underscored the significance of aligning the representation of culture in these GenAI systems with people’s socio-cultural ecosystems in modern technological literacies. We conclude with design principles for a more critical and holistic approach to AI literacy.

"TogetherTales RPG" is an augmented reality (AR) platform designed for children aged 4 to 6, aiming to foster prosocial behavior through interactive and collaborative role-playing. TogetherTales RPG is inspired by children’s design ideas related to technology supported social inclusion and prosocial skill development, a theme prevalent in children’s submissions in light of pandemic socialization restrictions. TogetherTales RPG integrates classic tabletop role-playing game mechanics with advanced AI and AR technologies to immerses children in a narrative-driven world where their personalized avatars interact with virtual elements and collaborate with peers to solve challenges. This blend of imaginative role-play and real-world social interaction facilitates prosocial skill development in a fun, engaging, and developmentally appropriate way.

Popular and news media often portray teenagers with sensationalism, as both a risk to society and at risk from society. As AI begins to absorb some of the epistemic functions of traditional media, we study how teenagers in two countries speaking two languages: 1) are depicted by AI, and 2) how they would prefer to be depicted. Specifically, we study the biases about teenagers learned by static word embeddings (SWEs) and generative language models (GLMs), comparing these with the perspectives of adolescents living in the U.S. and Nepal. We find English-language SWEs associate teenagers with societal problems, and more than 50% of the 1,000 words most associated with teenagers in the pretrained GloVe SWE reflect such problems. Given prompts about teenagers, 30% of outputs from GPT2-XL and 29% from LLaMA-2-7B GLMs discuss societal problems, most commonly violence, but also drug use, mental illness, and sexual taboo. Nepali models, while not free of such associations, are less dominated by social problems. Data from workshops with N=13 U.S. adolescents and N=18 Nepalese adolescents show that AI presentations are disconnected from teenage life, which revolves around activities like school and friendship. Participant ratings of how well 20 trait words describe teens are decorrelated from SWE associations, with Pearson's rho=.02, n.s. in English FastText and rho=.06, n.s. GloVe; and rho=.06, n.s. in Nepali FastText and rho=-.23, n.s. in GloVe. U.S. participants suggested AI could fairly present teens by highlighting diversity, while Nepalese participants centered positivity. Participants were optimistic that, if it learned from adolescents, rather than media sources, AI could help mitigate stereotypes. Our work offers an understanding of the ways SWEs and GLMs misrepresent a developmentally vulnerable group and provides a template for less sensationalized characterization.

Multimodal AI models capable of associating images and text hold promise for numerous domains, ranging from automated image captioning to accessibility applications for blind and low-vision users. However, uncertainty about bias has in some cases limited their adoption and availability. In the present work, we study 43 CLIP vision-language models to determine whether they learn human-like facial impression biases, and we find evidence that such biases are reflected across three distinct CLIP model families. We show for the first time that the the degree to which a bias is shared across a society predicts the degree to which it is reflected in a CLIP model. Human-like impressions of visually unobservable attributes, like trustworthiness and sexuality, emerge only in models trained on the largest dataset, indicating that a better fit to uncurated cultural data results in the reproduction of increasingly subtle social biases. Moreover, we use a hierarchical clustering approach to show that dataset size predicts the extent to which the underlying structure of facial impression bias resembles that of facial impression bias in humans. Finally, we show that Stable Diffusion models employing CLIP as a text encoder learn facial impression biases, and that these biases intersect with racial biases in Stable Diffusion XL-Turbo. While pretrained CLIP models may prove useful for scientific studies of bias, they will also require significant dataset curation when intended for use as general-purpose models in a zero-shot setting.

In this paper, we present a new visual block-based programming system designed for children to process, analyze, and visualize data. We introduce the system and describe how it was used during a series of 7 workshops with 27 children. During the workshops, children played the role of investigators and followed a storyline as part of the system to conduct data analyses to help the story’s protagonist locate a missing family member. We present our findings as a framework of computational data literacy that builds on the dimensions of Computational Thinking proposed by Brennan and Resnick [8], with a focus on aspects that are specific to using programming for data processing, analysis, and visualization. We conclude with a series of recommendations for future designers of systems to support the development of computational data literacy.

Increased technological capacity to collect and use data has created both new possibilities for benefiting individuals and societies, and critical questions of what is acceptable and just [31]. Because early definitions of data literacy have often excluded aspects of power, equity, empowerment, and emancipation, children’s learning experiences have focused more on the potential benefits compared to the critical questions. In this review article, we examine the importance of teaching critical data literacy to children as a key aspect of developing fluency with data. Using constructionist principles [67] as a guiding framework, we synthesize 48 educational research and design approaches that engage youth with data projects. We describe how these projects provide students with information about data’s origins and perspectives, and assist them in identifying, analyzing, and presenting data. Finally, we provide design implications and concrete examples on how constructionist approaches can be utilized for teaching critical data literacy.