AI literacy initiatives in K-12 education frequently emphasize the importance of AI ethics. However, many existing AI ethics curricula offer limited opportunities for students to examine ethical dilemmas or practice ethical decision-making with real-world scenarios. Prior work suggests that games can support ethics education by providing low-stakes, playful environments for exploration and reflection. In this paper, we present Orbiting the VirtueVerse, a scenario-based ethics game designed to introduce middle-school students to virtue ethics in the context of artificial intelligence. We report findings from a research workshop with 22 students aged 13–14. Participants learned about virtue ethics, played the game, and co-designed new scenarios for a future iteration of the game. Our findings suggest that game-based ethical scenarios are a promising approach for supporting students’ ethical understanding of AI. We conclude by offering design guidelines for effectively integrating ethical scenarios into educational games for AI literacy.

The use of generative AI in K-12 schools is surging despite a lack of appropriate guidance. Although prior technical AI literacy has incorporated more ethical considerations, there is little work on teaching students a concrete, practical, actionable AI use plan for specific learning tasks. This paper presents an AI-literacy curriculum integrated in the English Language Arts (ELA) Argumentation unit that centers on "use". Using the debate topic “Does Using AI in School Cause Cognitive Debt?” for deliberation and policymaking for resolution, the curriculum provides a learning trajectory that gives students insights into appropriate AI use through research and personal experience, eventually helping them develop their own informed plan. Early pilot evidence with 15 students suggests a significant increase in developing an informed plan for AI use.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of thirteen AI assignments from the 2025 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu

Power dynamics through the lens of autonomy in human-robot interaction (HRI) has largely been considered through the robot's persuasion and its impact on users. Power found in hierarchies and relationships between users and robots has been less investigated. Through a co-design workshop with industry employees, we investigate how employees design autonomous agents (AA) for a risk management and safety task, focusing on the designed agent interactions and personalities (robots and chatbots). The qualitative analysis of the storyboards and personalities revealed how employees designed autonomy and power dynamics between the workers and AAs, mirroring power dynamics in social systems. These results revealed how power dynamics are interconnected with anthropomorphization of AAs, demonstrating what has been previously theorized by HRI scholars. Overall, this work considers autonomy and power in design representations and unifies varying conceptualizations of power in HRI to support a holistic perspective of how power is explored through HRI design.

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.

The Feature Detection tool is a web-based activity that allows students to detect features in images and build their own rule-based classification algorithms. In this paper, we introduce the tool and share how it is incorporated into two, 45-minute lessons. The objective of the first lesson is to introduce students to the concept of feature detection, or how a computer can break down visual input into lower-level features. The second lesson aims to show students how these lower-level features can be incorporated into rule-based models to classify higher-order objects. We discuss how this tool can be used as a "first step" to the more complex concept ideas of data representation and neural networks.

Escape rooms have become increasingly popular as a form of entertainment, in addition to being adopted by educators for their effectiveness in improving student engagement and learning. While they have been introduced in various educational contexts, from nursing to mathematics, and for different age groups, including K-12 and university students, little research has been conducted on the benefits of escape rooms for adult learning of artificial intelligence (AI). Furthermore, most escape room implementations lack relevance to real-world situations and challenges with using AI systems in the wild. This study explores the effectiveness of an escape room, DRONEscape, as a tool for teaching AI concepts to Air Force participants. The results suggest that escape rooms can most effectively facilitate engagement and collaboration, and have positive effects on learning AI concepts. This paper also provides considerations for improvements to future iterations of AI-themed escape rooms to enhance learning, collaboration, engagement, and enjoyment.

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.

Children are beginning to interact and develop rapport with social robots in their homes. These devices pose new concerns around marketing to children. These include questions of how advertisements can and should be embedded in a robot and the robot's persona and which methods of conveying advertisements to the user are deceptive. In this paper, we engage with 62 children ages 9–12 in an activity to design future robot advertising policies. Results demonstrate that children prefer robots to advertise to them through casual conversations, citing a more positive user experience and the benefit of personalized and conversationally relevant advertising. These findings illuminate a tension between child preferences and more deceptive advertising policies. Overall, the work presented in this paper prompts new design and legal policy questions for how and if robots should advertise to children.

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.

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.

If we expect our children to be driving technology design agendas in the future, we must first help them recognize that opinions and beliefs are baked into the technologies that we create and that these opinions may serve some groups of people more than others. In this paper, we discuss an ethical design activity completed by 19 middle school-aged children. The activity encourages students to see technical systems as socio-technical systems, to engage them in stakeholder analysis, and to apply ethical design tools in order to redesign YouTube. Results indicate students are capable of transforming into critical users and ethical designers of technology. They are able to recognize the underlying design agendas for popular technologies such as YouTube, identify stakeholders who shape those design agendas, and apply an array of tools to reimagine technologies in a more inclusive manner.