The growing impact of AI on various fields, including art, highlights the importance of integrating AI learning into art education. This work investigates whether traditional art lessons can be adapted to meaningfully incorporate AI, focusing on its application to art-making practices. We adapted a character design activity to incorporate AI at different stages, such as using AI for creating references, getting feedback, visual design, animation, and personality design. We developed a character design learning activity which was supplemented by a code notebook and a front-end character design tool. 39 middle and high school students participated in this activity during two in-person Art and AI workshops. Analysis of creative outputs, knowledge surveys, and classroom discussions showed that students showed significant shifts in their understanding of AI as a creative collaborator, their art making practice, and their confidence with using AI tools. Learners demonstrated different creative styles while adopting AI into their character design. This approach demonstrates the potential for integrating AI into art lessons and offers a scalable framework for other non-CS subjects.

This research designs and applies inclusive child-robot interactions for collaborative creativity, where elementary school children and a social robot collaboratively create and publish picture stories. The robot offers creativity scaffolding during parts of the creative process of storytelling through social interactions such as feedback, question asking, divergent thinking, and positive reinforcement. The collaborative tasks and robot interactions are personalized for neurodivergent children's unique needs. Through a five-session user study with 32 children (ages 5–9) over 8 months, we investigate the impact of the social robot on children's exhibited creativity in storytelling over time, their creative interactions with the robot, and their perceptions of the robot as a creative collaborator. Our research revealed that inclusive design practices eliminated creative barriers for children with neurodevelopmental disorders. The robot's creativity scaffolding interactions positively influenced children's verbal creativity in storytelling, and had an influence on their storytelling creative process. After multiple sessions interacting with the robot, we observed the emergence of diverse creator styles among neurodivergent learners. We propose Inclusive Co-creative Child-robot Interaction (ICCRI) guidelines for fostering creativity in children, and accommodating diverse creator styles in complex, open-ended creative tasks.

Text-to-image generation (TTIG) technologies are Artificial Intelligence (AI) algorithms that use natural language algorithms in combination with visual generative algorithms. TTIG tools have gained popularity in recent months, garnering interest from non-AI experts, including educators and K-12 students. While they have exciting creative potential when used by K-12 learners and educators for creative learning, they are also accompanied by serious ethical implications, such as data privacy, spreading misinformation, and algorithmic bias. Given the potential learning applications, social implications, and ethical concerns, we designed 6-hour learning materials to teach K-12 teachers from diverse subject expertise about the technical implementation, classroom applications, and ethical implications of TTIG algorithms. We piloted the learning materials titled “Demystify text-to-image generative tools for K-12 educators" with 30 teachers across two workshops with the goal of preparing them to teach about and use TTIG tools in their classrooms. We found that teachers demonstrated a technical, applied and ethical understanding of TTIG algorithms and successfully designed prototypes of teaching materials for their classrooms.

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.

In an era where Artificial Intelligence (AI) permeates our lives, its impact on children raises critical considerations. This workshop aims to delve into the multifaceted realm of Human-Centered AI (HCAI) for children, exploring the transformative role of AI in fostering creative expression and inclusive learning environments. Our goal is to unite diverse research expertise and methodologies, focussing on how AI can be tailored to meet diverse learning needs, enabling personalized and engaging educational experiences which support creativity. This workshop will bring together researchers, educators, technologists, and practitioners for expert talks, interactive demonstrations, and collaborative discussions,. Our goal is to foster a multidisciplinary dialogue on developing child-centered AI solutions that enhance creative learning while being mindful of inclusivity, ethical considerations and safeguarding against potential risks.

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.

An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. There is little work in using game-based learning methods in AI literacy. Games are known to be compelling media to teach children about complex STEM concepts. In this work, we developed a competitive card game for middle and high school students called “AI Audit” where they play as AI start-up founders building novel AI-powered technology. Players can challenge other players with potential harms of their technology or defend their own businesses by features that mitigate these harms. The game mechanics reward systems that are ethically developed or that take steps to mitigate potential harms. In this paper, we present the game design, teacher resources for classroom deployment and early playtesting results. We discuss our reflections about using games as teaching tools for AI literacy in K-12 classrooms.

In this demo, we present PaintBall, a tool that facilitates creative art creation and manipulation from movement data. It is designed as a public computation project aimed to foster conversation and connection between youth with different interests (specifically art, sports, or computing) at a community center around the shared activity of creating these rich visualizations and art pieces. We expect the design features of PaintBall – public art and tinkering work, cross interest engagement, and discrete artifact generation – to be key ideas that can be used across a variety of contexts and enable rich community development among youth by providing novel touchstones for conversation as well as reflection on these preexisting activities themselves. The following URL will host a working demo of this project – https://tiilt.northwestern.edu/projects/sportsense/paintball

AI curricula are being developed and tested in classrooms, but wider adoption is premised by teacher professional development and buy-in. When engaging in professional development, curricula are treated as set in stone, static and educators are prepared to offer the curriculum as written instead of empowered to be leaders in efforts to spread and sustain AI education. This limits the degree to which teachers tailor new curricula to student needs and interests, ultimately distancing students from new and potentially relevant content. This paper describes an AI Educator Make-a-Thon, a two-day gathering of 34 educators from across the United States that centered co-design of AI literacy materials as the culminating experience of a year-long professional development program called Everyday AI (EdAI) in which educators studied and practiced implementing an innovative curriculum for Developing AI Literacy (DAILy) in their classrooms. Inspired by the energizing and empowering experiences of Hack-a-Thons, the Make-a-Thon was designed to increase the depth and longevity of the educators' investment in AI education by positively impacting their sense of belonging to the AI community, AI content knowledge, and their self confidence as AI curriculum designers. In this paper we describe the Make-a-Thon design, findings, and recommendations for future educator-centered Make-a-Thons.

Generative Artificial Intelligence (AI) models are a compelling way to introduce K-12 students to AI education using an artistic medium, and hence have drawn attention from K-12 AI educators. Previous Creative AI curricula mainly focus on Generative Adversarial Networks (GANs) while paying less attention to Autoregressive Models, Variational Autoencoders (VAEs), or other generative models, which have since become common in the field of generative AI. VAEs' latent-space structure and interpolation ability could effectively ground the interdisciplinary learning of AI, creative arts, and philosophy. Thus, we designed a lesson to teach high school students about VAEs. We developed a web-based game and used Plato's cave, a philosophical metaphor, to introduce how VAEs work. We used a Google Colab notebook for students to re-train VAEs with their hand-written digits to consolidate their understandings. Finally, we guided the exploration of creative VAE tools such as SketchRNN and MusicVAE to draw the connection between what they learned and real-world applications. This paper describes the lesson design and shares insights from the pilot studies with 22 students. We found that our approach was effective in teaching students about a novel AI concept.

In this work, we explore the effect of a social robot’s embodiment and creativity scaffolding on children’s creative problem solving skills in the context of a digital creative problem-solving game called Escape!Bot. Children aged 5-11 years played the video game, which involved assembling contraptions to escape a digital world, and the robot Jibo acted as a collaborative peer that offered questions, reflective prompts, challenges, and ideas. In order to evaluate the role of the robot’s co-presence and creativity scaffolding, we ran a 2x2 experiment to determine the factorial efficacy of the robot’s embodiment and creativity scaffolding behaviors. We observed mixed results, with the robot’s creativity scaffolding having a positive influence on the time taken to complete the game, but not on the overall use of novel objects or reuse of objects. We present the system design, user study and findings from Escape!Bot to investigate the feasibility of designing social robots to support creative problem solving.

In this hands-on workshop participants will experience the curricula from three NSF funded projects, which engage youth in creating art with and about AI technologies while exploring related ethical concerns.

Applications of Generative Machine Learning techniques such as Generative Adversarial Networks (GANs) are used to generate new instances of images, music, text, and videos. While GANs have now become commonplace on social media, a part of children’s lives, and have considerable ethical implications, existing K-12 AI education curricula do not include generative AI. We present a new module, “What are GANs?”, that teaches middle school students how GANs work and how they can create media using GANs. We developed an online, team-based game to simulate how GANs work. Students also interacted with up to four web tools that apply GANs to generate media. This module was piloted with 72 middle school students in a series of online workshops. We provide insight into student usage, understanding, and attitudes towards this lesson. Finally, we give suggestions for integrating this lesson into AI education curricula.

The Contour to Classification game is a browser-based game that teaches middle school students basic concepts in supervised learning. The game is an online variant of the Neural Network game that was presented at AAAI Fall Symposium Teaching AI in K-12 track in 2019. We share preliminary findings from implementing the online version of the original Neural Network game in a pilot research study and describe the game’s evolution to the Contour to Classification game. The new game uses a simulation of a neural network to engage students, through digital drawing and selection interactions, in the classification of images. The players act as nodes in a multi-step process of compositing salient smaller features to form larger features and ultimately a partial contour of an object that is used to make a prediction. After evaluating the prediction, information is sent back through the network in processes mimicking back propagation and gradient descent. Additional rounds of the game can be played to witness how the network evolves and gets “better” at classifying images from contours. Through this game, we aimed for students to learn the structure, components, and functioning of a neural network, and the processes involved in supervised learning. The Contour to Classification game supports online student learning by providing the image classification experience using purely visual inputs to each layer. We will conclude with a discussion of if and how the evolving design addresses classroom needs and scaling considerations.

Applications of generative models such as Generative Adversarial Networks (GANs) have made their way to social media platforms that children frequently interact with. While GANs are associated with ethical implications pertaining to children, such as the generation of Deepfakes, there are negligible efforts to educate middle school children about generative AI. In this work, we present a generative models learning trajectory (LT), educational materials, and interactive activities for young learners with a focus on GANs, creation and application of machine-generated media, and its ethical implications. The activities were deployed in four online workshops with 72 students (grades 5-9). We found that these materials enabled children to gain an understanding of what generative models are, their technical components and potential applications, and benefits and harms, while reflecting on their ethical implications. Learning from our findings, we propose an improved learning trajectory for complex socio-technical systems.

In this experience report, we describe an AI summer workshop designed to prepare middle school students to become informed citizens and critical consumers of AI technology and to develop their foundational knowledge and skills to support future endeavors as AI-empowered workers. The workshop featured the 30-hour "Developing AI Literacy" or DAILy curriculum that is grounded in literature on child development, ethics education, and career development. The participants in the workshop were students between the ages of 10 and 14; 87% were from underrepresented groups in STEM and Computing. In this paper we describe the online curriculum, its implementation during synchronous online workshop sessions in summer of 2020, and preliminary findings on student outcomes. We reflect on the successes and lessons we learned in terms of supporting students' engagement and conceptual learning of AI, shifting attitudes toward AI, and fostering conceptions of future selves as AI-enabled workers. We conclude with discussions of the affordances and barriers to bringing AI education to students from underrepresented groups in STEM and Computing.

Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.

Children's creativity contributes to their learning outcomes and personal growth. Standardized measures of creative thinking reveal that as children enter elementary school, their creativity drops. In this work, we evaluated whether a social robotic peer can help 6-10-year-old children think creatively by demonstrating creative behavior. We designed verbal and non-verbal behaviors of the social robot that constitute interaction patterns for artificial creativity. 51 participants played the Droodle Creativity Game with the robot to generate creative titles for ambiguous images. One group of participants interacted with the creative robot, and one group interacted with the non-creative robot. Participants that interacted with the creative robot generated significantly higher number of Droodle titles, expressed greater variety in titles, and scored higher on the Droodles' creativity. We observe that children can model a social robotic peer's creativity, and hence inform robot interaction patterns for artificial creativity that can foster creativity in children.