Recommendation systems shape much of what people, including youth, encounter online, influencing their exposure to information and ideas. Understanding their workings and potential downsides, such as filter bubbles, is increasingly important. At the same time, Minecraft remains one of the most popular and accessible game platforms among students worldwide, making it a promising medium for AI literacy outreach. Building on a previous in-person augmented reality application called BeeTrap, in which players, acting as bees, pollinate flowers and see how similar choices narrow their environment, we created BeeTrap-MC, a Minecraft-based version aimed at broader reach and accessibility. Unlike the original facilitator-led group workshop, BeeTrap-MC is designed for students individual playthrough. We conducted a study with nine middle school participants, using pre-/post-assessments and qualitative interviews to evaluate its effectiveness. Results showed significant learning gains in key AI concepts, such as understanding filter bubbles and their consequences. We also discuss key differences in design, usability and outcomes between BeeTrap-MC and the original, reflecting on trade-offs in adapting a group-based embodied experience into a shorter, self-guided digital format.

Children increasingly interact with conversational AI in everyday settings, yet existing evaluation approaches rarely capture how trust, doubt, and understanding develop across childhood. This second edition of the C3AI workshop brings together researchers, designers, and educators to explore where trust, design, and evaluation meet in child-AI interaction. Through hands-on analysis of naturalistic interaction transcripts from children aged 6-14 and research-informed personas, participants will collaboratively identify cues of trust, confusion, resistance, and critique. Small-group activities and co-design exercises will support the development of preliminary, developmentally sensitive evaluation metrics for trust. Workshop outcomes include shared personas, draft evaluation templates, and open materials to support child-centred, responsible AI design and assessment.

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 children face global challenges, creating environments that nurture hope and empower them to shape a fair, transparent future is essential.Conversational AI systems (CAIs) offer opportunities for cognitive and emotional growth, with trust built through transparent, responsive interactions.This workshop offers participants a hands-on opportunity to analyze child-CAI interactions, bringing their own use cases alongside pre-recorded examples from five countries provided by organizers.In collaboration with a diverse group of stakeholders, the focus will be on identifying the human factors that influence trust in child-AI interactions, aiming to advance guidelines for building transparent, trustworthy conversational AI systems.

Hearing parents of young deaf and hard-of-hearing (DHH) children often lack essential skills in American Sign Language (ASL), which can lead to socio-emotional isolation and language deprivation for DHH children.Learning ASL, especially non-manual signs (NMS), can be challenging for hearing individuals due to cognitive and cultural barriers.Inspired by "group narrative", a collaborative storytelling activity commonly seen in the Deaf communities, we propose a novel collaborative learning approach named CoSignPlay.It aims to support NMS learning among hearing family members and DHH children by allowing two players to jointly control NMS and manual signs (MS) of a 3D avatar in a game context.We adopted the design probe and technology probe methods to explore the unique opportunities and challenges of CoSignPlay.We conducted an interview study with six hearing parents of young DHH children, six ASL instructors, and two speech-language pathologists in early education programs.Findings revealed positive feedback to CoSign-Play in addressing key cognitive and cultural challenges of NMS learning for novice hearing learners, along with insightful critiques and suggestions for improvements.We present in-depth discussions of design implications and guidelines for future collaborative learning technologies on NMS.

AI recommendations influence our daily decisions. The convenience of navigating personalized content goes hand-in-hand with the notorious filter bubble effect, which may decrease people’s exposure to diverse options and opinions. Children are especially vulnerable to this due to their limited AI literacy and critical thinking skills. In this study, we propose a novel Augmented Reality (AR) application BeeTrap. It aims to not only raise children’s awareness of filter bubbles but also empower them to mitigate this ethical issue through sense-making of AI recommendation systems’ inner workings. By having children experience and break filter bubbles in a flower recommendation system, BeeTrap utilizes embodied metaphors (e.g., NEAR-FAR, ITERATION) and analogies (bee pollination) to bridge abstract AI concepts with sensory-motor experiences in familiar STEM contexts. To evaluate our design’s effectiveness and accessibility for a broad range of children, we introduced BeeTrap in a four-day summer camp for middle-school students from underrepresented backgrounds in STEM. Results from pre- and post-tests and interviews show that BeeTrap developed students’ technical understanding of AI recommendations, empowered them to break filter bubbles, and helped them foster new personal and societal perspectives around AI technologies.

The vast majority of deaf or hard-of-hearing (DHH) children are born to hearing parents. Due to a lack of immersive exposure to their natural language - sign language - they are at severe risk of language deprivation. In response to this challenge, this paper presents a novel computer-mediated communication platform named Tabletop Interactive Play System (TIPS). It serves as a test-bed to investigate technical and ethical solutions that enable hearing parents to use American Sign Language (ASL) during face-to-face play with their with their DHH children. The TIPS platform offers a variety of user options in three key aspects: (1) ASL recommendation in alignment with hearing parents’ real-time speech; (2) ASL display through different form-factors (Augmented Reality (AR) projection, tablet, and smart glasses); and (3) autonomy support to enhance users’ sense of agency and trust in the system. In this paper, we will describe the system’s design, implementation, and preliminary evaluation results.

Over 90 percent of Deaf and Hard of Hearing (DHH) children in the United States are born to hearing parents, who have little to no command of American Sign Language (ASL). This leaves the majority of DHH children at risk of language deprivation in early childhood. This study investigates the design space of Augmented Reality (AR) and wearable technologies in supporting hearing parents to offer sign language environments for young DHH children. We conducted an online survey with 65 participants (hearing/DHH parents and teachers of DHH children aged 6 months to 5 years) to gather preferences and interests of technologies that support hearing parents to deliver ASL on-the-fly, and stay attentive to the DHH child’s visual attention during joint toy play. We found that Near-Object Projection is most preferred for real-time ASL delivery, and haptic feedback is most preferred for raising the parent’s awareness of a child’s attention. Results also show a strong interest in using the proposed technologies in interacting with and maintaining joint attention with DHH children on a daily basis. We discuss key design recommendations that inform the design of future technologies that support just-in-time and contextual-aware communication in ASL, with minimal obtrusion to face-to-face interaction.

Parental language input in early childhood plays a critical role in lifelong neuro-cognitive and social development. Deaf and Hard of Hearing (DHH) children are often at risk of language deprivation due to hearing parents’ limited knowledge of sign language - the natural language for DHH children at birth. To offer an immersive sign language environment for DHH children, we designed a novel computer-mediated communication technology named Table Top Interactive System (TIPS). It aims to provide context-responsive recommendation of American Sign Language (ASL) in real-time for hearing parents during face-to-face joint play with their DHH children. The system emphasizes supporting parent autonomy by adapting ASL recommendations using parent’s speech during play, and minimizes obtrusion for face-to-face interaction through an Augmented Reality (AR) display. This paper describes the design and development of an initial working prototype of TIPS and preliminary results of the system’s efficiency regarding system latency and accuracy for ASL recommendation and visualization. Next, we plan to conduct a user study to gather expert and parent feedback about the system design and ASL recommendation strategies for long-term and personalized usage.