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Reinforcement learning (RL) is increasingly important to AI literacy, yet accessible K-12 tools for introducing RL concepts to students remain scarce. We developed RewardGarden, a game-based learning environment in which middle school students take on the role of a hummingbird collecting nectar under varying reward structures. The browser-based game is complemented by comprehensive educator materials, including a playful narrative introduction to RL, a game tutorial, and a facilitation guide with discussion prompts. In this paper, we describe the design of the learning environment, share insights from a classroom pilot, and provide implementation guidance. To understand implementation and gather feedback, we piloted the game with 92 middle school students across three classrooms. Our experience highlights the promise of game-based approaches for democratizing RL education, while identifying important practical considerations: the value of multi-session designs, the need for balanced reward feedback, and the critical role of scaffolding for complex concepts like exploration-exploitation tradeoffs. We share detailed implementation guidance, the publicly available game, and educator materials to support adoption. 

As advancements in AI increasingly shape professional and societal practice, the landscape of teaching crucial AI concepts is shifting rapidly. At the undergraduate level, it is critical for computer science students to develop not only technical proficiency but also foundational AI literacy that supports reasoning about how AI-driven systems work and where they may succeed or fail. This paper reports on a pilot study that investigates the adaptation of three game-based learning activities, originally created for informal middle grades educational settings, into an upper-level undergraduate AI course. The activities are grounded in the AI4K12 Five Big Ideas in AI and focus on a range of concepts, including search and reinforcement learning. Findings indicate strong student engagement and suggest that the activities can serve as effective entry points for discussing higher-level AI concepts, while also revealing gaps in students’ mental models of AI systems. The results highlight both the promise and the limits of reusing youth-oriented game-based learning activities in undergraduate contexts and point to design considerations for scaling AI literacy experiences across educational levels. 

Collaboration is widely promoted as a strategy for advancing equity in computing education, yet the claims are rarely theorized. This Perspectives paper synthesizes sociocultural, interdependence, and identity-based theories to offer theoretical insights and provocations that guide equity-centered research, instructional design, and community discourse. We argue that collaboration can foster sustained participation in computing by redistributing epistemic authority and supporting learners’ sense of belonging, but may also reinforce inequities when roles, norms, and interactional structures go unexamined. We focus on upper-elementary contexts as critical sites where inequities in computing participation begin to take shape, particularly for learners historically marginalized in computing, including girls, Black and Latinx students, rural learners, and students with limited prior access to computing. 

Abstract This paper offers a new perspective on the valuation of the impacts of industrial ocean pollution. Rising levels of industrial pollutants have a profound impact on marine resource-dependent peoples, particularly those dependent on seafood consumption. We argue that current regulation of these pollutants is both insufficient and inequitable, as it only accounts for impacts on physical health while ignoring cultural implications. This paper introduces the “cultural tipping point” as a new framework that integrates the impacts of ocean pollution on peoples’ physical and cultural health and well-being. Drawing on anthropology, marine sciences, public health, and critical Indigenous studies, the cultural tipping point synthesizes diverse concepts of “cultural keystone species,” food sovereignty, and industrial pollution. Ultimately, our goal is to make the cultural impacts of ocean pollution legible within global governance networks, and to advocate for greater allocation of societal resources to address this issue. 

AI concepts are increasingly integrated into subjects such as science, yet little is known about how upper elementary students perceive AI after participating in classroom-based AI learning experiences. This poster reports attitudinal findings from 28 fifth-grade students who participated in a science-integrated AI unit. A 15-item Likert instrument measured self-efficacy, value, interest, and perceptions of AI’s societal impact. Students reported strong confidence in learning and using AI and endorsed its importance and future utility; however, interest was lower than confidence and value beliefs. Students largely rejected the idea that AI is dangerous, suggesting limited engagement with risk-oriented perspectives. Findings indicate that science-integrated AI instruction can foster early self-efficacy and perceived relevance, while highlighting the need to intentionally cultivate sustained interest and developmentally appropriate critical AI literacy. 

As large language models (LLMs) and chatbots become increasingly prevalent, there is an urgent need to create engaging, age-appropriate learning activities that foster foundational AI literacy with a focus on natural language processing (NLP). This paper presents the iterative design and implementation of three instructional activities that introduce middle school learners (ages 11--14) to NLP concepts through playful, hands-on experiences aligned with the AI4K12 Big Idea of Natural Interaction. These activities include: (1) an unplugged card game that develops students' understanding of embeddings and similarity, (2) an unplugged collaborative sentence-generation challenge that illustrates how language models work, and (3) a web-based educational game in which students design and interact with chatbots. Each activity was implemented and refined across multiple educational contexts, including teacher professional development workshops, summer camps, and classroom implementations.All activities are designed to be easy to set up, requiring only commonly available classroom technology (e.g., laptops) and a few inexpensive materials (e.g., decks of cards), and are supported with facilitation guides and reflection prompts. Early implementations revealed areas for refinement, leading to clearer scaffolding that helped students connect gameplay to underlying NLP concepts, and post-refinement surveys indicated that students found the activities both enjoyable and educational. Findings suggest that blending unplugged and digital formats enhances comprehension, and that tailoring content to students' local contexts supports engagement. By making these activities openly available, this work contributes to the growing ecosystem of K–12 AI education resources and offers practical guidance for integrating NLP concepts into classroom instruction.