There is an increasing need for young people to become critically AI literate, understanding not only how AI works but also its limitations and ethical nuances. Yet, designing learning experiences that make such complex, serious topics engaging remains a challenge. This paper explores transformational games as a promising approach for supporting youth learning about generative AI (GenAI) and ethics. We designed and implemented two games, Diversity Duel and Secret Agent, that integrate GenAI tools with gameplay elements. This work investigates how the games' elements: (1) peer evaluation, (2) constraint-based creativity, and (3) social deduction supported socio-ethical reasoning about GenAI. Participants recognized and debated bias in GenAI outputs, connected these patterns to real-world inequities, and developed nuanced understandings of bias. Participants further came to see how prompt design shapes AI behavior. Our findings suggest that group-based games with these elements can support fostering critical AI literacy.

Educational programming languages (EPL), for all their success in enabling computing education at scale, regularly exclude learners by embedding assumptions about ability, class, culture, language fluency, and identity. Further, most EPL designs are not governed in ways that are responsive to the needs of learners and their communities on the margins of computing, raising questions about how the design processes behind EPL could be organized to ensure they serve everyone equitably. Building upon discourse on diversity, educational justice, and design justice, we propose seven justice-centered design requirements for EPL, arguing that they should be accessible , liberatory , transparent , cultural , obtainable , democratic , and enduring . For each, we examine why these requirements are necessary and offer examples of languages that do and do not meet them. Throughout, we surface constraints that EPL impose on being justice-centered and grand challenges for research to be able to overcome them.

Sustainability education increasingly calls for innovative learning environments that help learners recognize ecological interdependencies and challenge anthropocentric worldviews. Everyday multispecies relationships, such as with companion animals, often underexplored, offer opportunities for cultivating ecological literacy and care. This paper introduces Augmented Ecological Relating (AER), an approach that combines Augmented Reality (AR) with embodied inquiry to explore multispecies perspectives. Going beyond embodied inquiry, AER specifies how digital augmentation can systematically support learners' iterative noticing, ethical reasoning, and action within everyday multispecies ecosystems. We draw on a virtual summer workshop for adolescents in which participants used AR filters simulating dog and cat vision to investigate their pets' sensory worlds. We used qualitative case study methods to examine how AR tools mediated human youths' noticing, inquiry, and reflection. We found that the AR filters used in the study's context enabled participants to critically reconsider pet behaviors within home ecologies. Participants recognized companion animals as ecological beings with distinct sensory experiences, explored interconnections among humans, animals, and environments, and reflected on ethical responsibilities in multispecies relationships. Through iterative inquiry, youth moved beyond companionship to sustainability-oriented perspectives grounded in relational care, systems thinking, and practical action. By embedding digital augmentation into everyday contexts, AER enabled learners to engage with more-than-human perspectives, fostering ecological awareness, ethical reflection, and sustainability literacy in accessible, meaningful ways.

Usability testing with experts and potential users can assess the effectiveness, efficiency, and user satisfaction of graphical user interfaces (GUIs) but doing so remains a costly and time-intensive process. Prior work has used computer use agents (CUAs) and other generative agents that can simulate user interactions and preference, but we show that agents still struggle to provide accurate usability assessments. In this work, we present a novel machine learning method that operationalizes a computational definition of usability to train CUAs to assess GUI usability by i) prioritizing important interaction flows, ii) executing them through human-like interactions, and iii) predicting a learned numerical usability score. We train a computer use agent, uxCUA, with our algorithm on a large-scale dataset of fully interactive user interfaces (UIs) paired with usability labels and human preferences. We show that uxCUA outperforms larger models in accurate usability assessments and produces realistic critiques of both synthetic and real UIs. More broadly, our work aims to build a principled, data-driven foundation for automated usability assessment in HCI.

In response to the exponential growth in the use of artificial intelligence and machine learning applications, educators, researchers and policymakers have taken steps to integrate artificial intelligence applications into K-12 education. Among these efforts, one equally important approach has received little, if any attention: What if students and teachers were not just learning to be competent users of AI but also its creators? This question is at the heart of CreateAI in which K12 educators, researchers, and learning scientists addressed the following questions: (1) What tools, skills, and knowledge will empower students and teachers to build their own AI/ML applications? (2) How can we integrate these approaches into classrooms? and (3) What new possibilities for learning emerge when students and teachers become innovators and creators? In the report we provide recommendations for what tools designed for creating AI/ML applications should address in terms of design features, and learner progression in investigations. To promote effective learning and teaching of creating AI applications, we also need to help students and teachers select appropriate tools. We outline how we need to develop a better understanding of learning practices and funds of knowledge to support youth as they create and evaluate AI/ML applications. This also includes engaging youth in learning about ethics and critically that is authentic, empowering, and relevant throughout the design process. Here we advocate for the integration of ethics in the curriculum. We also address what teachers need to know and how assessments can help establish baselines, include different instruments, and promote students as responsible creators of AI. Together, these recommendations provide important insights for preparing students to engage thoughtfully and critically with these technologies.

As AI increasingly saturates our daily lives, it is crucial that youth develop skills to critically use and assess AI systems and envision better alternatives. We apply theories from culturally responsive computing to design and study a learning experience meant to support Black Muslim teen girls in developing critical literacy with generative AI (GenAI). We investigate fashion design as a culturally-rich, creative domain for youth to apply GenAI and then reflect on GenAI's socio-ethical aspects in relation to their own intersectional identities. Through a case study of a three-day, voluntary informal education program, we show how fashion design with GenAI exposed affordances and limitations of current GenAI tools. As the girls used GenAI to create realistic depictions of their dream fashion collections, they encountered socio-ethical limitations of AI, such as biased models and malfunctioning safety systems that prohibited their generation of outputs that reflected their creative ideas, bodies, and cultures. Discussions anchored in the phenomenology of impossible creative realization supported participants' development of critical AI literacy and descriptions of how preferable, identity-affirming technologies would behave. Our findings contribute to the field's growing understanding of how computing education experience designs linking creativity and identity can support critical AI literacy development.

Despite ongoing efforts to diversify engineering, underrepresented minority (URM) students face persistent systemic barriers to equitable participation. Beyond just access, culturally relevant pedagogy (CRP) and near-peer mentorship have shown to improve student engagement and retention. However, the currently sparse pool of URM STEM graduates limits students' access to formally educated mentors of similar cultural and ethnic backgrounds, challenging the scalability of representative CRP-based engineering programs. Free AI tools like large language models (LLMs) can offer novice programmers natural language support for debugging code and exploring new concepts, but it remains unclear whether they can provide enough technical support to help scale URM-led engineering education programs. We investigate the potential for LLMs, specifically ChatGPT 3.5, to meaningfully support CRP in an embedded systems summer course taught at a community center within the AVELA - A Vision for Engineering Literacy & Access framework. We ask how URM students will naturally adopt ChatGPT within the educational program when presented without scaffolding: as a learning tool to enhance their knowledge of class-related content, or as a toy that distracts from it? Analysis of classroom observations, student surveys, final presentations, and individual ChatGPT logs revealed a disconnect between students' personal interest and the course objectives. While students were able to use ChatGPT for course-related learning, they rarely did so. Instead, they primarily used it to enhance their knowledge of topics related to personal interest, suggesting a lack of motivation or perceived relevance of ChatGPT as a tool, rather than a lack of ability to use it. These preliminary findings suggest that without intentional integration and guidance, CRP frameworks cannot expect students to effectively leverage ChatGPT to enhance engineering related content.

Multimodal UI design and development tools that interpret sketches or natural language descriptions of UIs inherently have notations: the inputs they can understand. In AI-based systems, notations are implicitly defined by the data used to train these systems. In order to create usable and intuitive notations for interactive design systems, we must regard, design, and evaluate these training datasets as notation specifications. To better understand the design space of notational possibilities for future design tools, we use the Cognitive Dimensions of Notations framework to analyze two possible notations for UI sketching. The first notation is the sketching rules for an existing UI sketch dataset, and the second notation is the set of sketches generated by participants in this study, where individuals sketched UIs without imposed representational rules. We imagine two systems, FixedSketch and FlexiSketch, built with each notation respectively, in order to understand the differential affordances of, and potential design requirements for, systems. We find that participants' sketches were composed of element-level notations that are ambiguous in isolation but are interpretable in context within whole designs. For many cognitive dimensions, the FlexiSketch notation supports greater intuitive creative expression and affords lower cognitive effort than the FixedSketch notation, but cannot be supported with prevailing, element-based approaches to UI sketch recognition. We argue that for future multimodal design tools to be truly human-centered, they must adopt contemporary AI methods, including transformer-based and human-in-the-loop, reinforcement learning techniques to understand users' context-rich expressive notations and corrections.

Much of early literacy education happens at home with caretakers reading books to young children. Prior research demonstrates how having dialogue with children during co-reading can develop critical reading readiness skills, but most adult readers are unsure if and how to lead effective conversations. We present ContextQ, a tablet-based reading application to unobtrusively present auto-generated dialogic questions to caretakers to support this dialogic reading practice. An ablation study demonstrates how our method of encoding educator expertise into the question generation pipeline can produce high-quality output; and through a user study with 12 parent-child dyads (child age: 4-6), we demonstrate that this system can serve as a guide for parents in leading contextually meaningful dialogue, leading to significantly more conversational turns from both the parent and the child and deeper conversations with connections to the child's everyday life.

Unequal technology access for Black and Latine communities has been a persistent economic, social justice, and human rights issue despite increased technology accessibility due to advancements in consumer electronics like phones, tablets, and computers. We contextualize socio-technical access inequalities for Black and Latine urban communities and find that many students are hesitant to engage with available technologies due to a lack of engaging support systems. We present a holistic student-led STEM engagement model through AVELA - A Vision for Engineering Literacy and Access leveraging culturally responsive lessons, mentor embodied community representation, and service learning. To evaluate the model's impact after 4 years of mentoring 200+ university student instructors in teaching to 2,500+ secondary school students in 100+ classrooms, we conducted 24 semi-structured interviews with college AnonymizedOrganization members. We identify access barriers and provide principled recommendations for designing future STEM education programs.

This workshop will bring together researchers and educators to imagine a future of low-cost, widely-available digital making for children, both within the STEAM classroom and beyond. In particular, we are interested in expanding the reach of digital making with programmable microcontrollers (such as Arduino, the BBC micro:bit, etc.) to underrepresented children in the STEAM fields, which includes historically excluded or marginalized children as well as those lacking access to computers and/or the Internet. Participants will report on their experience helping children learn about digital technology while creating wearables, robotics, environmental sensors and more. Participants who submit a position paper or work-in-progress report will have an opportunity to present their work and ideas. From these presentations, we will select emerging themes to discuss.

Recent studies have shown that pedagogical approaches like hands-on lessons, representative and near-peer mentoring, as well as culturally responsive teaching increase Science Technology Engineering and Math (STEM) engagement in classrooms, specifically those with underrepresented minority (URM) students. URM students interested in pursuing STEM show increased engagement and confidence from more holistic outreach programs, however there is a dearth of instructors who represent URM student identities who also have the necessary technical know-how. However, new AI tools based on Large Language Models (LLMs), like GPT-3.5, have been shown to increase the productivity of software developers, with the largest productivity gains being for non-experts. Therefore, we propose a study on the usability of LLMs as an educational tool for supporting instructors of various skill levels in both facilitating and scaling programming classes for URM students. We aim to evaluate the capacity for these AI tools to help reduce the digital divide by exploring the limitations, effectiveness, and potential hesitations URM communities may have with integrating LLMs into their classrooms. If LLMs can support, and/or help scale the number of, culturally responsive mentors capable of instructing programming courses, URM students in the US may be poised as one of the major beneficiaries of these new AI tools.