With childhood increasingly mediated by AI and marked by children’s heightened vulnerabilities, critical reflection emerges as a vital tool for both understanding and strengthening children’s ethical reasoning, steering them between uncritical adoption and blanket pessimism about AI. As such, we present outcomes of a study with 66 children (aged 10-11) wherein we trace what children attend to and how they perceive AI ethics. Aligned with UNESCO’s ethical principles for AI, we utilised 10 design fiction scenarios set in familiar contexts to prompt reflection. Mixed-methods data showed that children’s perceptions skewed towards caution; ethical concerns were also distributed unevenly across principles, indicating where AI ethics literacy may need targeted scaffolding. This work contributes to HCI by highlighting the complexity of children’s perceptions and showing how speculative, reflection-based methods can shift children’s ethical considerations about AI, with three recommendations for AI ethics literacy education that the HCI community should consider in future.

Generative AI's growing use in content creation significantly impacts societal perceptions by perpetuating and reinforcing gender stereotypes.The amplification of stereotypes in AI-generated content can lead to increased discrimination, exclusion, misinformation, and contribute to racial and gender disparities.To address this challenge, we explore the direct impact of generative AI on gender attribution and stereotype reinforcement in digital imagery through a survey with 111 participants, analysing interpretations of gender expression in 216 AI-generated images.Findings reveal a pronounced bias toward masculine-leaning attributions, particularly in images where gender identity is labelled as androgyne.This research provides three key contributions: (1) an in-depth understanding of how people perceive gender expressions in AI-generated images; (2) a dataset of 216 images evaluated by participants for masculinity, femininity, and neutrality; and (3) two key challenges to consider in order to address the stereotyped representations of gender expressions in AI-generated content, highlighting the need for more inclusive AI practices.

The increasing adoption of generative AI raises concerns regarding its potential to reinforce gender stereotypes, especially in content aimed at children.AI-generated images that reflect traditional gender roles can inadvertently influence children's perceptions, embedding unconscious biases.Through co-design sessions with domain experts, we developed a child-friendly generative AI web application designed with dual purpose: (1) analysing children's choices to determine potential underlying bias and whether children's own gender identity influences these choices, and (2) investigating if specific non-aesthetic traits prompt the AI to produce characters with stereotypical presentations.

The integration of social robots into children’s environments is becoming increasingly pertinent, spanning from entertainment to health care. Although prior studies highlight the role of morphology in shaping children’s perceptions of these machines, there is little research to examine their perceptions of social robots in various contexts. Our research investigates how different morphologies (anthropomorphic, zoomorphic, and mechanomorphic) influence children’s emotional responses. We involved 36 children (9-11 years old) in a design fiction-based study examining how morphology impacts children’s hopes and fears of social robots in varying scenarios and contexts, categorising the results into four distinct themes that represent children’s general perceptions (autonomy, cognition, socio-emotional, and physical). From this, we identify two distinct design challenges, and discuss how these may impact both researchers general users of social robots with children. Further, we emphasise the need for careful and considerate deployment of social robots for children in both research and real-world applications.

The influence of generative Artificial Intelligence (AI) on the propagation and amplification of societal biases, particularly in the context of children’s content creation, is a growing concern. By developing and testing a prototype tool designed to assist children in Digital Storytelling (DST), our research aimed to explore and mitigate the propagation of stereotypes through the use of a character-generating AI tool utilising Stable Diffusion. Despite initial aspirations, the tool demonstrated significant biases inherent in the underlying AI model, leading to the decision against its use by children. The findings we discovered contribute to a broader discourse on the development of ethical AI and its use, advocating for a more responsible and inclusive approach to technological innovation in the context of children’s digital media consumption and creation.