Generative AI tools introduce new and accessible forms of media creation for youth. They also raise ethical concerns about the generation of fake media, data protection, privacy and ownership of AI-generated art. Since generative AI is already being used in products used by youth, it is critical that they understand how these tools work and how they can be used or misused. In this work, we facilitated students’ generative AI learning through expression of their imagined future identities. We designed a learning workshop - Dreaming with AI - where students learned about the inner workings of generative AI tools, used text-to-image generation algorithms to create their imaged future dreams, reflected on the potential benefits and harms of generative AI tools and voiced their opinions about policies for the use of these tools in classrooms. In this paper, we present the learning activities and experiences of 34 high school students who engaged in our workshops. Students reached creative learning objectives by using prompt engineering to create their future dreams, gained technical knowledge by learning the abilities, limitations, text-visual mappings and applications of generative AI, and identified most potential societal benefits and harms of generative AI.

Artificial Intelligence (AI) is permeating almost every area of society, reshaping how many people, including youth, navigate the world. Despite the increased presence of AI, most people lack a baseline knowledge of how AI works. Moreover, social barriers often hinder equal access to AI courses, perpetuating disparities in participation in the field. To address this, it is crucial to design AI curricula that are effective, inclusive, and relevant, especially to learners from backgrounds that are historically excluded from working in tech. In this paper, we present AI for Wellbeing, a curriculum where students explore conversational AI and the ethical considerations around using it to promote wellbeing. We specifically designed content, educator materials, and educational technologies to meet the interests and needs of students and educators from diverse backgrounds. We piloted AI for Wellbeing in a 5-day virtual workshop with middle school teachers and students. Then, using a mixed-methods approach, we analyzed students' work and teachers' feedback. Our results suggest that the curriculum content and design effectively engaged students, enabling them to implement meaningful AI projects for wellbeing. We hope that the design of this curriculum and insights from our evaluation will inspire future efforts to create culturally relevant K-12 AI curricula.

App Planner is an interactive support tool for K-12 students, designed to assist in creating mobile applications. By utilizing generative AI, App Planner helps students articulate the problem and solution through guided conversations via a chat-based interface. It assists them in brainstorming and formulating new ideas for applications, provides feedback on those ideas, and stimulates creative thinking. Here we report usability tests from our preliminary study with high-school students who appreciated App Planner for aiding the app design process and providing new viewpoints on human aspects especially the potential negative impact of their creation.

Today, Artificial Intelligence (AI) is prevalent in everyday life, with emerging technologies like AI companions, autonomous vehicles, and AI art tools poised to significantly transform the future. The development of AI curricula that shows people how AI works and what they can do with it is a powerful way to prepare everyone, and especially young learners, for an increasingly AI-driven world. Educators often employ robotic toolkits in the classroom to boost engagement and learning. However, these platforms are generally unsuitable for young learners and learners without programming expertise. Moreover, these platforms often serve as either programmable artifacts or pedagogical agents, rarely capitalizing on the opportunity to support students in both capacities. We designed Doodlebot, a mobile social robot for hands-on AI education to address these gaps. Doodlebot is an effective tool for exploring AI with grade school (K-12) students, promoting their understanding of AI concepts such as perception, representation, reasoning and generation. We begin by elaborating Doodlebot's design, highlighting its reliability, user-friendliness, and versatility. Then, we demonstrate Doodlebot's versatility through example curricula about AI character design, autonomous robotics, and generative AI accessible to young learners. Finally, we share the results of a preliminary user study with elementary school youth where we found that the physical Doodlebot platform was as effective and user-friendly as the virtual version. This work offers insights into designing interactive educational robots that can inform future AI curricula and tools.

Although artificial intelligence (AI) is increasingly involved in everyday technologies, AI literacy amongst the general public remains low. Thus many AI education curricula for people without prior AI experience have emerged, often utilizing graphical programming languages for hands-on projects. However, there are no tools that assist educators in evaluating learners’ AI projects or provide learners with contemporaneous feedback on their work. We developed LevelUp, an automatic code analysis tool to support these educators and learners. LevelUp is built into a block-based programming platform and gives users continuous feedback on their text classification projects. We evaluated the tool with a crossover user study where participants completed two text classification projects, once where they could access LevelUp and once when they could not. To measure the tool’s impact on participants’ understanding of text classification, we used pre-post assessments and graded both of their projects against LevelUp’s rubric. We saw a significant improvement in the quality of participants’ projects after they used the tool. We also used questionnaires to solicit participants’ feedback. Overall, participants said that LevelUp was useful and intuitive. Our investigation of this novel automatic assessment tool can inform the design of future code analysis tools for AI education.

Today’s artificially intelligent (AI) technologies are more pervasive, persuasive, and alluring than those of the past. They affect how many people, including the youngest members of society, socialize, learn, and play. According to a survey from Common Sense Media, the average teenager spends almost three hours on social media or video hosting websites every day [1] . Common Sense Media also reported that 72% of teens believed that the algorithms controlling newsfeeds and advertisements on these websites intentionally try to manipulate users’ attention and interests [2] . Moreover, a study done by Juniper Research [3] forecasted that 70 million U.S. households, or 55% of the total households in the U.S., will have installed a smart speaker by the end of 2022. Research on children and AI shows that children often form friendly, trusting relationships with their virtual assistants and smart toys which could make them vulnerable [4] . Despite the prevalence of AI in daily life, most people are unaware of AI’s role in society and lack a baseline knowledge of how AI works.

This paper describes ML Blocks, https://tinyurl.com/ml-blocks, a novel interface for training, evaluating, and deploying Tiny Machine Learning (TinyML) models. TinyML is a fast-growing field that incorporates powerful machine learning algorithms into everyday technologies such as activity trackers and Internet of Things devices. Although TinyML-capable microcontrollers are popular in computer science education, few students have had the opportunity to learn about the field because of a lack of novice-friendly ML interfaces. With ML Blocks, users assemble data sets, define, and train neural network classifiers, within one unified block interface. Users can quickly evaluate their classifiers using built-in visualization tools and then export them for use in microcontroller projects. ML Blocks makes the end-to-end development of TinyML models easier for physical computing students and tinkerers at all levels.

Body-tracking artificial intelligence (AI) systems like Kinect games, Snapchat Augmented Reality (AR) Lenses, and Instagram AR Filters are some of the most engaging ways students experience AI in their everyday lives. Additionally, many students have existing interests in physical hobbies like sports and dance. In this paper, we present PoseBlocks; a suite of block-based programming tools which enable students to build compelling body-interactive AI projects in any web browser, integrating camera/microphone inputs and body-sensing user interactions. To accomplish this, we provide a custom block-based programming environment building on the open source Scratch project, introducing new AI-model-powered blocks supporting body, hand, and face tracking, emotion recognition, and the ability to integrate custom image/pose/audio models from the online transfer learning tool Teachable Machine. We introduce editor functionality such as a project video recorder, pre-computed video loops, and integration with curriculum materials. We discuss deploying this toolkit with an accompanying curriculum in a series of synchronous online pilots with 46 students, aged 9-14. In analyzing class projects and discussions, we find that students learned to design, train, and integrate machine learning models in projects of their own devising while exploring ethical considerations such as stakeholder values and algorithmic bias in their interactive AI systems.

To enable a diverse citizenry to fully participate in future society, we must prepare all students to construct and critique emerging technologies like Artificial Intelligence (AI). Classrooms are important spaces to teach students these skills, however there are few AI curricula that have been developed for and used by K-12 teachers. We developed the \textit{How to Train Your Robot: AI and Ethics Curriculum} for middle school teachers who want to introduce AI to their students. This paper describes the curriculum and professional development we used to prepare teachers to run a five-day AI course. Before and after they ran the curriculum, we interviewed teachers to understand their opinions on pedagogical approaches to teaching AI, meeting students' needs, and the feasibility of doing the activities in the classroom. Our results indicate that, with appropriate training, even teachers who were new to computer science felt prepared and successfully engaged their students in the topic. We hope our insights will inform future efforts to realize AI education in primary and secondary classrooms.

In recent years, Artificial Intelligence (AI) has become increasingly prevalent in our lives. Because of this, individuals of all ages need to be aware of how AI works. To introduce middle school students to AI concepts, we built a text classifier extension into a block-based programming interface that allows students to train custom machine learning models. To make the extension more accessible, a translator was incorporated where the language of each input is automatically detected and translated to English. Our classifier's accuracy was comparable to similar classifiers such as Machine Learning for Kids' text classifier and Uclassify's text classifier, and its effectiveness was tested against these classifiers with two test datasets. We piloted the classifier with middle school students in an online AI course. The students first learned the concepts behind the classifier which consisted of word embeddings, K-Nearest-Neighbors, and classification bias. They were then able to use the text classifier to create their own projects. Some of the projects were a snake identifier, a TV show suggester, a chat robot, and a healthcare robot. With this extension, students were able to engage in project-based learning to become more knowledgeable about the ever-growing field of AI and raise their awareness about applications of AI within their own lives.

We developed the How to Train Your Robot curriculum to empower middle school students to become conscientious users and creators of Artificial Intelligence (AI). As AI becomes more embedded in our daily lives, all members of society should have the opportunity to become AI literate. Today, most deployed work in K-12 AI education takes place at strong STEM schools or during extracurricular clubs. But, to promote equity in the field of AI, we must also design curricula for classroom use at schools with limited resources. How to Train Your Robot leverages a low-cost ($40) robot, a block-based programming platform, novice-friendly model creation tools, and hands-on activities to introduce students to machine learning. During the summer of 2020, we trained in-service teachers, primarily from Title 1 public schools, to deliver a five-day, online version of the curriculum to their students. In this work, we describe how students' self-directed final projects demonstrate their understanding of technical and ethical AI concepts. Students successfully selected project ideas, taking the strengths and weaknesses of machine learning into account, and implemented an array of projects about everything from entertainment to science. We saw that students had the most difficulty designing mechanisms to respond to user feedback after deployment. We hope this work inspires future AI curricula that can be used in middle school classrooms.

Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.

PopBots is a hands-on toolkit and curriculum designed to help young children learn about artificial intelligence (AI) by building, programming, training, and interacting with a social robot. Today’s children encounter AI in the forms of smart toys and computationally curated educational and entertainment content. However, children have not yet been empowered to understand or create with this technology. Existing computational thinking platforms have made ideas like sequencing and conditionals accessible to young learners. Going beyond this, we seek to make AI concepts accessible. We designed PopBots to address the specific learning needs of children ages four to seven by adapting constructionist ideas into an AI curriculum. This paper describes how we designed the curriculum and evaluated its effectiveness with 80 Pre-K and Kindergarten children. We found that the use of a social robot as a learning companion and programmable artifact was effective in helping young children grasp AI concepts. We also identified teaching approaches that had the greatest impact on student’s learning. Based on these, we make recommendations for future modules and iterations for the PopBots platform.

We developed a novel early childhood artificial intelligence (AI) platform, PopBots, where preschool children train and interact with social robots to learn three AI concepts: knowledge-based systems, supervised machine learning, and generative AI. We evaluated how much children learned by using AI assessments we developed for each activity. The median score on the cumulative assessment was 70% and children understood knowledge-based systems the best. Then, we analyzed the impact of the activities on children's perceptions of robots. Younger children came to see robots as toys that were smarter than them, but their older counterparts saw them more as people that were not as smart as them. Children who performed worse on the AI assessments believed that robots were like toys that were not as smart as them, however children who did better on the assessments saw robots as people who were smarter than them. We believe early AI education can empower children to understand the AI devices that are increasingly in their lives.

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.

Social robots are increasingly being developed for long-term interactions with children in domains such as healthcare, education, therapy, and entertainment. As such, we need to deeply understand how children's relationships with robots develop through time. However, there are few validated assessments for measuring young children's long-term relationships. In this paper, we present a pilot test of four assessments that we have adapted or created for use in this context with children aged 5--6: the Inclusion of Other in Self task, the Social-Relational Interview, the Narrative Description, and the Self-disclosure Task. We show that children can appropriately respond to these assessments with reasonably high internal reliability, and that the proposed assessments are able to capture child-robot relationship adjustments over a long-term interaction. Furthermore, we discuss gender and population differences in children's responses.

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.