Figure 1: Cognimates interface showing coding blocks, AI chat, and image generation features.

Prior research has investigated children’s perceptions of algorithmic bias, but provides little guidance on engaging children in conversations on algorithmic bias that center their agency and well-being. To address this, we developed discussions and design activities based on three scenarios of algorithmic (un)fairness. We conducted these discussions and activities with 16 children (ages 8-12) in the US, and examined our data using qualitative thematic analysis. Grounded in lived experiences and situated knowledge, participants were capable of reasoning around both explicit and implicit effects of algorithmic bias. Participants also expressed distrust of technology, doubting technology’s abilities and preferring human approaches to resolve unfairness. This work contributes (1) a more nuanced understanding of children’s situated reasoning of technology, suggesting their potential for critical engagement and (2) a blueprint for engaging children in scaffolded yet open-ended sensemaking around algorithmic fairness, informing the design of tools, curricula, and other learning experiences for children.

This paper examines the art practices, artwork, and motivations of prolific users of the latest generation of text-to-image models. Through interviews, observations, and a user survey, we present a sampling of the artistic styles and describe the developed community of practice around generative AI. We find that: 1) artists hold the text prompt and the resulting image can be considered collectively as a form of artistic expression (prompts as art), and 2) prompt templates (prompts with “slots” for others to fill in with their own words) are developed to create generative art styles. We discover that the value placed by this community on unique outputs leads to artists seeking specialized vocabulary to produce distinctive art pieces (e.g., by reading architectural blogs to find phrases to describe images). We also find that some artists use “glitches” in the model that can be turned into artistic styles of their own right. From these findings, we outline specific implications for design regarding future prompting and image editing options.

Prior work has broadly explored empowering children to learn to program by making video games. However, such work has rarely considered the role of families in this learning, leaving many open questions about how inter-generational collaborations might support and constrain learning. To investigate these opportunities, we conducted a family-based study of TileCode, a rule-based programming platform for video-game programming, and scaffolded a 4-week series of game programming activities with 19 children (9 to 14 years old) and 16 parents. Using a joint media engagement lens to analyze family knowledge and programming strategies, we found: 1) families demonstrated many dynamic collaboration patterns distinct from pair programming and other collaboration models, 2) parents played a unique role in scaffolding and guiding more complex designs and programming tasks, 3) families found it challenging to start their games from scratch but benefited greatly from having programming patterns for particular game behaviors. These findings suggest the need for game programming platforms to design around the unique kinds of collaboration in inter-generational domain-specific programming.

Many families engage daily with artificial intelligence (AI) applications, from conversations with a voice assistant to mobile navigation searches. While there are known ways for youth to learn about AI, we do not yet understand how to engage parents in this process. To explore parents’ roles in helping their children develop AI literacies, we designed 11 learning activities organized into four topics: image classification, object recognition, interaction with voice assistants, and unplugged AI co-design. We conducted a 5-week online in-home study with 18 children (5 to 11 years old) and 16 parents. We identify parents’ most common roles in supporting their children and consider the benefits of parent-child partnerships when learning AI literacies. Finally, we discuss how our different activities supported parents’ roles and present design recommendations for future family-centered AI literacies resources.

Artificial Intelligence (AI) educational resources such as training tools, interactive demos, and dedicated curriculum are increasingly popular among educators and learners. While prior work has examined pedagogies for promoting AI literacy, it has yet to examine how well technology resources support these pedagogies. To address this gap, we conducted a systematic analysis of existing online resources for AI education, investigating what learning and teaching affordances these resources have to support AI education. We used the Technological Pedagogical Content Knowledge (TPACK) framework to analyze a final corpus of 50 AI resources. We found that most resources support active learning, have digital or physical dependencies, do not include all the five big ideas defined by AI4K12 guidelines, and do not offer built-in support for assessment or feedback. Teaching guides are hard to find or require technical knowledge. Based on our findings, we propose that future AI curricula move from singular activities and demos to more holistic designs that include support, guidance, and flexibility for how AI technology, concepts, and pedagogy play out in the classroom.

Children are increasingly surrounded by AI technologies but can overestimate smart devices’ abilities due to their lack of transparency. Drawing on the sense-making theory, this study explores how children come to see machine intelligence after training custom machine learning models and creating smart programs that use them. Through a 4-week observational study in after-school programs with 52 children (7 to 12 years old), we found that children engage in the scientific method while training, coding and testing their smart programs. We also found that children became more skeptical of certain abilities of smart devices as they shifted their attribution of agency from the devices to the people who program them. These changes in perception happened both through individual interactions with agents and prompted debates with peers. Based on these results, we conclude with discussions on strategies for promoting children’s sense-making practices and sense of agency in the age of machine learning.

Intelligent toys and smart devices are becoming ubiquitous in children's homes. As such, it is imperative to understand how these computational objects impact children's development. Children's attribution of intelligence relates to how they perceive the behavior of these agents [6]. However, their underlying reasoning is not well understood. To explore this, we invited 30 pairs of children (4--10 years old) and their parents to assess the intelligence of mice, robots, and themselves in a maze-solving activity. Participants watched videos of mice and robots solving a maze. Then, they solved the maze by remotely navigating a robot. Solving the maze enabled participants to gain insight into the agent's mind by referencing their own experience. Children and their parents gave similar answers for whether the mouse or the robot was more intelligent and used a wide variety of explanations. We also observed developmental differences in childrens' references to agents' social-emotional attributes, strategies and performance.

Today's children are growing up with smart toys, Internet-connected devices that use artificial intelligence to drive interactive play. In a prior research study, we found that children ages 4--10 perceive these toys as worthy of trust [5]. This leads us to inquire if children in this age range could be directly influenced by these devices. In this work, we used a conformity test and a disobedience task to study how children are influenced by a talking doll. We found that the doll could influence children to change their judgments about moral transgressions, however it was unsuccessful in persuading children to disobey an instruction. Finally, we analyzed children's perceptions of the smart toy and discusses implications of this work for future child-agent interaction.

Autonomous technology is becoming more prevalent in our daily lives. We investigated how children perceive this technology by studying how 26 participants (3-10 years old) interact with Amazon Alexa, Google Home, Cozmo, and Julie Chatbot. We refer to them as "agents" in the context of this paper. After playing with the agents, children answered questions about trust, intelligence, social entity, personality, and engagement. We identify four themes in child-agent interaction: perceived intelligence, identity attribution, playfulness and understanding. Our findings show how different modalities of interaction may change the way children perceive their intelligence in comparison to the agents'. We also propose a series of design considerations for future child-agent interaction around voice and prosody, interactive engagement and facilitating understanding.