Adolescents struggle to understand bias and representation in AI, particularly the concept of how datasets used in machine learning can be representative of populations or not. Within early experiments in teaching about investigating bias in AI systems using the Developing AI Literacy (DAILy) curriculum, we observed participating youth struggling to understand what it meant to be represented in the output of AI tools. For example, when using Google Image Search with prompts such as “physicist” and “outdoor recreation,” participating youth did not understand the question, “Are you represented in this outcome?” We saw an opportunity to address this challenge using the Kapor Foundation's Responsible AI and Tech Justice Guide. Drawing insights from three of the six core components of the framework presented in the guide, we developed Games of Representation (GR), a series of three card-based activities using SET game cards to teach concepts of population, sample, dataset, and representation. Through game play, players manipulate datasets, role-play as stakeholders with competing interests, and explore real-world scenarios where representation matters. The GR games and corresponding guide provide educators, families, and care providers with flexible, hands-on activities for guided, playful conversations with adolescents about AI ethics. This work contributes practical resources for K-12 AI education while addressing critical gaps in youth understanding of statistical bias and stakeholder influence in AI development.

Key to the advancement of Artificial Intelligence (AI) Literacy is the identification of teaching practices used to advance youth understanding of AI concepts. AI curricula and teacher professional development have become increasingly accessible, yet little is known about in-service teachers' AI pedagogy in the classroom. We report findings from a 2-year longitudinal exploratory study of classroom AI teaching practices from 5 in-service middle school teachers. Results from a mixed-method analysis of interviews and surveys suggest that teaching practices initially emphasized AI technical content, yet the more effective second year practices focused on making AI concepts relevant using Culturally Responsive Pedagogy.

Artificial intelligence (AI) has rapidly pervaded and reshaped almost all walks of life, but efforts to promote AI literacy in K-12 schools remain limited. There is a knowledge gap in how to prepare teachers to teach AI literacy in inclusive classrooms and how teacher-led classroom implementations can impact students. This paper reports a comparison study to investigate the effectiveness of an AI literacy curriculum when taught by classroom teachers. The experimental group included 89 middle school students who learned an AI literacy curriculum during regular school hours. The comparison group consisted of 69 students who did not learn the curriculum. Both groups completed the same pre and post-test. The results show that students in the experimental group developed a deeper understanding of AI concepts and more positive attitudes toward AI and its impact on future careers after the curriculum than those in the comparison group. This shows that the teacher-led classroom implementation successfully equipped students with a conceptual understanding of AI. Students achieved significant gains in recognizing how AI is relevant to their lives and felt empowered to thrive in the age of AI. Overall this study confirms the potential of preparing K-12 classroom teachers to offer AI education in classrooms in order to reach learners of diverse backgrounds and broaden participation in AI literacy education among young learners.

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.

In this experience report, we describe an Artificial Intelligence (AI) Methods in Data Science (DS) curriculum and professional development (PD) program designed to prepare high school teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI to support their integration of AI methods into existing STEM classrooms. The curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Networks, and Transfer learning that follow a scaffolded learning progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, interactive web-based tools, and inspecting and modifying the code used to build, train and test AI models within Google Colab notebooks. The participants in the PD program were secondary school teachers from the Southwest and North-east regions of the United States who represented a variety of STEM disciplines: Biology, Chemistry, Physics, Engi-neering, and Mathematics. We share findings on teacher outcomes from the implementation of two one-week PD workshops during the summer of 2021 and share suggestions for improvements provided by teachers. We conclude with a discussion of affordances and challenges encountered in preparing teachers to integrate AI education into disciplinary classrooms.

This paper describes an AI Book Club as an innovative 20-hour professional development (PD) model designed to prepare teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI that are foundational to developing AI-literate citizens. The design of the intervention was motivated by a desire to manage the cognitive load of AI learning by spreading the PD program over several weeks and a desire to form and maintain a community of teachers interested in AI education during the COVID-19 pandemic. Each week participants spent an hour independently reading selections from an AI book, reviewing AI activities, and viewing videos of other educators teaching the activities, then met online for 1 hour to discuss the materials and brainstorm how they might adapt the materials for their classrooms. The participants in the AI Book Club were 37 middle school educators from 3 US school districts and 5 youth-serving organizations. The teachers are from STEM disciplines as well as Social Studies and Art. Eighty-nine percent were from underrepresented groups in STEM and CS. In this paper we describe the design of the AI Book Club, its implementation, and preliminary findings on teachers' impressions of the AI Book Club as a form of PD, thoughts about teaching AI in classrooms, and interest in continuing the book club model in the upcoming year. We conclude with recommendations for others interested in implementing a book club PD format for AI learning.

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.

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.

Our community has been seeing explosive interest and growth in K-12 computer science education. With this, a common question from states and school districts arises: What should students learn in a K-12 computer science pathway? We in the community are routinely asked to provide input on what is critical for students to learn at various grade levels.