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Artificial Intelligence (AI) and Natural Language Processing (NLP) have become increasingly relevant across multiple fields, creating a necessity for young learners to understand these concepts. However, resources enabling learners to apply AI and NLP, particularly in middle school science, remain limited. To address this gap, we present the early development of NLP4Science, an interactive visualization application facilitating the integration of NLP concepts such as sentiment analysis and keyword extraction into middle school science. We adopted an iterative co-design process starting with a professional development workshop with four teachers, followed by a 2-day pilot study with 48 eighth graders, and concluding with a 5-day study involving 50 sixth graders. This poster presents an overview of NLP4Science, highlighting its key features, and sharing insights gained from the iterative design process, demonstrating the potential of NLP4Science to transform AI and NLP learning within middle school science classrooms. 

There is growing awareness of the central role that artificial intelligence (AI) plays now and in children's futures. This has led to increasing interest in engaging K-12 students in AI education to promote their understanding of AI concepts and practices. Leveraging principles from problem-based pedagogies and game-based learning, our approach integrates AI education into a set of unplugged activities and a game-based learning environment. In this work, we describe outcomes from our efforts to co design problem-based AI curriculum with elementary school teachers. 

Recent years have seen the rapid adoption of artificial intelligence (AI) in every facet of society. The ubiquity of AI has led to an increasing demand to integrate AI learning experiences into K-12 education. Early learning experiences incorporating AI concepts and practices are critical for students to better understand, evaluate, and utilize AI technologies. AI planning is an important class of AI technologies in which an AI-driven agent utilizes the structure of a problem to construct plans of actions to perform a task. Although a growing number of efforts have explored promoting AI education for K-12 learners, limited work has investigated effective and engaging approaches for delivering AI learning experiences to elementary students. In this paper, we propose a visual interface to enable upper elementary students (grades 3-5, ages 8-11) to formulate AI planning tasks within a game-based learning environment. We present our approach to designing the visual interface as well as how the AI planning tasks are embedded within narrative-centered gameplay structured around a Use-Modify-Create scaffolding progression. Further, we present results from a qualitative study of upper elementary students using the visual interface. We discuss how the Use-Modify-Create approach supported student learning as well as discuss the misconceptions and usability issues students encountered while using the visual interface to formulate AI planning tasks. 

Recent years have seen growing recognition of the importance of enabling K-12 students to learn computer science. Meanwhile, artificial intelligence, a field of computer science, has with the potential to profoundly reshape society. This has generated increasing demand for fostering an AI-literate populace. However, there is little work exploring how to introduce K-12 students to AI and how to support K-12 teachers in integrating AI into their classrooms. In this work, we explore how to introduce AI learning experiences into upper elementary classrooms (student ages 8 to 11). With a focus on integrating AI and life science, we present initial work on a collaborative game-based learning environment that features rich problem-based learning scenarios that enable students to gain experience with AI applied toward solving real-world life-science problems. 

Narrative and collaboration are two core features of rich interactive learning. Narrative-centered learning environments offer significant potential for supporting student learning. By contextualizing learning within interactive narratives, these environments leverage students’ innate facilities for developing understandings through stories. Computer-supported collaborative learning environments offer students rich, collaborative learning experiences in which small groups of students engage in constructing artifacts, addressing disciplinary challenges, and solving problems. Narrative and collaboration have distinct affordances for learning, but combining them poses significant challenges. In this paper, we present initial work on solving this problem by introducing collaborative narrative-centered learning environments. These environments will enable small groups of students to collaboratively solve problems in rich multi-participant storyworlds. We propose a novel framework for designing and developing these environments, which we are using to create a collaborative narrative-centered learning environment for middle school ecosystems education. In the learning environment, students work on problem-solving scenarios centered on how to support optimal fish health in aquatic environments. Results from pilot testing the learning environment with 45 students suggest it supports the creation of engaging and effective collaborative narrative-centered learning experiences. 

Block-based programming languages reduce the need to learn low-level programming syntax while enabling novice learners to focus on computational thinking skills. Game-based learning environments have been shown to create effective and engaging learning experiences for students in a broad range of educational domains. The fusion of block-based programming with game-based learning offers significant potential to motivate learners to develop computational thinking skills. A key challenge educational game developers face in creating rich, interactive learning experiences that integrate computational thinking activities is the lack of an embeddable block-based programming toolkit. Current block-based programming languages, such as Blockly and Scratch, cannot be easily embedded into industry-standard 3D game engines. This paper presents IntelliBlox, a Blockly-inspired toolkit for the Unity cross-platform game engine that enables learners to create block-based programs within immersive game-based learning environments. Our experience using IntelliBlox suggests that it is an effective toolkit for integrating block-based programming challenges into game-based learning environments.