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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. 

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. 

In computer-supported collaborative learning environments, analyzing student dialogue is essential for understanding collaborative problem-solving behaviors and supporting effective learning. Prior work often treats all dialogue interactions uniformly, failing to capture how specific dialogue interaction differentially impact learning experiences and outcomes. To address this limitation, we introduce a dialogue-based learning analytics framework that integrates weighted temporal clustering of dialogue with large language model-based interpretation. Our framework identifies student interaction patterns most predictive of group learning gains and uses these insights to enable early prediction of learning outcomes and generate pedagogically meaningful interpretations. We evaluate our framework on collaborative dialogue from middle school students engaged in a collaborative game-based learning environment. Our results show that our framework achieves 83.1% accuracy in learning outcome prediction. In addition, expert evaluations and case studies demonstrate that the identified weighted dialogue patterns reflect key collaborative problem-solving behaviors recognized as important in collaborative learning. By surfacing high-impact interaction patterns and enabling prioritized interpretation generation, our framework provides a promising approach for accurately analyzing students’ collaborative dialogue. 

As artificial intelligence (AI) becomes increasingly embedded in daily life, there is a growing need to develop engaging and effective educational materials to foster K-12 students’ AI literacy. This paper presents the iterative design, development, and refinement of three playful learning activities focused on natural language processing (NLP), aimed at demystifying AI concepts for middle school students. The activities were implemented across two summer camps, with findings indicating increased engagement and improved understanding in the second camp. We also present design principles that emerged from this work to support the broader integration of AI literacy in educational settings. 

This case study explores the experiences of a non-computer science educator participating in a professional development program designed to support AI teaching in rural middle schools. Using Cultural-Historical Activity Theory and expansive learning as analytical lenses, the research examines how the educator leveraged supportive elements within her environment to overcome challenges, gradually building confidence while adopting new teaching practices. Findings underscore the need for tailored professional development, ongoing support, and progressive teacher learning for effective AI education. This study contributes to understanding how non-computer science educators can be supported in bringing AI learning experiences to their students, thereby making AI education more accessible. 

This study investigates the implementation of a classroom response system in STEM education in a higher education context. The study used ExplainIt, a web-based classroom response system designed to support students’ self-explanations and provide instant feedback. Data were collected from 32 undergraduate students using four instruments including demographic information, self-efficacy, engagement, and system evaluation. The results showed that students reported positive learning experiences, demonstrated increased self-efficacy in STEM content, and indicated high levels of engagement following their use of ExplainIt.