As generative AI becomes increasingly integrated into children's creative lives, designing responsible and meaningful tools for Child-AI co-creation is a growing concern in HCI and education.This paper presents a scoping review of 20 studies on Child-AI co-creation, analyzing the types of creative activities involved, the age groups studied, and the roles AI systems play.Based on this synthesis, we identify six key design considerations: protecting child data privacy, minimizing bias and hallucinations, fostering appropriate reliance on AI, balancing support and creative freedom, encouraging peer and family collaboration, and making AI's creative process understandable.This work contributes an initial framework to guide the design of child-centered and developmentally appropriate AI co-creation systems, and highlights directions for future research.

media and content creation literacies becomes critical.Yet little research examines how children engage with video creation or how emerging technologies, such as generative AI, can support this creative process.This study explores children's engagement and challenges during a two-week, project-based learning workshop where they learned to create educational videos teaching school-aligned science topics.Our exploratory findings suggest that educational video creation activity, when designed properly, can serve as an intervention for the dual learning of science literacy CCS Concepts• Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing; Collaborative interaction.

In video media platforms, children are predominantly consumers and learners of educational content. However, they can also be content creators and educators. Existing research has delved into children’s behavior in engaging audiences as YouTube video creators, while our study specifically investigates children as teachers in tutorial videos. We conducted a content analysis of 129 YouTube how-to videos featuring children to understand their teaching behaviors and family involvement. We found that children showed several learning by teaching behaviors, such as discussing materials and procedures and addressing an imagined audience directly. However, they less frequently discussed the principle behind the task, asked the audience questions, or reflected on the task. We also found varying levels of parental engagement. While parents safeguarded, guided, reminded, and taught, they talked less about principles and tended to explain rather than asking questions. We also discussed a number of implications for future research and design based on the findings.

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 viral launch of new generative AI (GAI) systems, such as ChatGPT and Text-to-Image (TTL) generators, sparked questions about how they can be effectively incorporated into writing education. However, it is still unclear how teachers, parents, and students perceive and suspect GAI systems in elementary school settings. We conducted a workshop with twelve families (parent-child dyads) with children ages 8-12 and interviewed sixteen teachers in order to understand each stakeholder’s perspectives and opinions on GAI systems for learning and teaching writing. We found that the GAI systems could be beneficial in generating adaptable teaching materials for teachers, enhancing ideation, and providing students with personalized, timely feedback. However, there are concerns over authorship, students’ agency in learning, and uncertainty concerning bias and misinformation. In this article, we discuss design strategies to mitigate these constraints by implementing an adults-oversight system, balancing AI-role allocation, and facilitating customization to enhance students’ agency over writing projects.

Artificial intelligence (AI) has had a profound impact on the medical industry. As the ultimate consumers, patients’ acceptance of medical AI plays an important role in realizing its widespread adoption in healthcare. However, previous studies have indicated that patients’ acceptance of medical AI is still inconsistent. This study considered the application of medical AI in different scenarios and divided them into medical AI for diagnosis and treatment (MAI-DT) and medical AI for non-diagnosis and non-treatment (MAI-NDT). Drawing on the theories of uniqueness and dehumanization, this study explored the impacts of patients’ sense of uniqueness (SOU) on their acceptance of medical AI under the two scenarios and the underlying mechanisms. Analysis of the data from 238 samples collected during the COVID-19 period in China showed that participants with a higher SOU had a lower acceptance of MAI-DT but a higher acceptance of MAI-NDT than participants with a lower SOU. In addition, perceived AI knowledge and perceived AI empathy mediated the relationship between the participants’ SOU and their acceptance of AI under the two scenarios. The theoretical and practical implications are discussed.

In early and middle childhood, the development of social and emotional skills is crucial. Traditional approaches often rely on curriculum-based, teacher-led methods, which may restrict children’s interactions and independent exploration. On the other hand, technology-enhanced solutions typically focus on recognizing children’s emotions without truly helping them to understand their feelings in a meaningful way. To address this gap, we introduce EmotionBlock, a novel physical toolkit with wooden blocks specifically designed for children aged 3-6. EmotionBlock encourages the development of emotional management and social skills through immersive storytelling and introspection. This hands-on approach enables children to engage in unstructured exploration and narrative creation, while simultaneously offering adults a tool to guide and support the learning experience.

The study examines the design implications of leveraging generative AI tools such as ChatGPT, Stable Diffusion, Midjourney for literacy development and creative expression for children [6, 8, 18]. We sought to elicit insights on the applicability of generative AI for educational purposes from various stakeholders (i.e., parents, teachers, and AI researchers). We recruited nine participants to elicit their perspectives on designing a visual narrative app with generative AI. We examined the opportunities and limitations of the current generative AI tools. Using the implications from our evaluation, we propose AIStory, an AI-powered visual storytelling application prototype that can be used for children’s creative expression, storytelling, and literacy development.