Artificial intelligence (AI) is quickly finding broad application in every sector of society. This rapid expansion of AI has increased the need to cultivate an AI-literate workforce, and it calls for introducing AI education into K-12 classrooms to foster students’ awareness and interest in AI. With rich narratives and opportunities for situated problem solving, story-driven game-based learning offers a promising approach for creating engaging and effective K-12 AI learning experiences. In this paper, we present our ongoing work to iteratively design, develop, and evaluate a story-driven game-based learning environment focused on AI education for upper elementary students (ages 8 to 11). The game features a science inquiry problem centering on an endangered species and incorporates a Use-Modify-Create scaffolding framework to promote student learning. We present findings from an analysis of data collected from 16 students playing the game's quest focused on AI planning. Results suggest that the scaffolding framework provided students with the knowledge they needed to advance through the quest and that overall, students experienced positive learning outcomes.

Recent years have seen growing awareness of the need to advance AI literacy for K-12 students to empower them in understanding, evaluating, and using AI. Automated problem solving is a fundamental aspect of AI, enabling machines to mimic human problemsolving abilities. Fostering awareness and interest in AI capabilities such as automated problem solving should begin early, including in the elementary grades. Although AI planning can be a complex topic, leveraging the benefits of game-based learning offers a promising approach to engage young children in learning about this important AI concept. In this work, we explore the interactions and outcomes of upper elementary students (ages 8 to 11) playing a quest on AI planning embedded within a game-based learning environment. Results indicate that students experienced positive learning gains from pre-test to post-test, while analyzing trace data from the game provides insights into challenges students faced as they attempted the in-game missions.

As the presence of artificial intelligence (AI) technologies increases throughout everyday life, so does the need to engage rural communities in AI learning experiences, as these communities often have limited access to such educational opportunities. This work presents three game-based learning activities rooted in core AI concepts: natural language processing, search, and reinforcement learning. These activities were implemented in a summer camp with middle grades students in a rural area of the USA. We share an overview of the activities, as well as key observations and takeaways from student responses in post-activity surveys.

There is a growing need for 21st-century workers to be digitally literate and to possess computational thinking and collaborative problem-solving skills. Computer-supported collaborative learning (CSCL) focused on computational thinking can guide students toward the co-development of these skills. In this work, we present our approach to integrating virtual and physical learning modalities into InfuseCS, a CSCL environment. InfuseCS uses problem-based learning scenarios to situate upper elementary school students (ages 8 to 11) in a CSCL setting to foster their computational thinking and science knowledge construction as they collaborate to create digital narratives.

The growing ubiquity of artificial intelligence (AI) is reshaping much of daily life. This in turn is raising awareness of the need to introduce AI education throughout the K-12 curriculum so that students can better understand and utilize AI. A particularly promising approach for engaging young learners in AI education is game-based learning. In this work, we present our efforts to embed a unit on AI planning within an immersive game-based learning environment for upper elementary students (ages 8 to 11) that utilizes a scaffolding progression based on the Use-Modify-Create framework. Further, we present how the scaffolding progression is being refined based on findings from piloting the game with students.

The ubiquity of artificial intelligence (AI) in everyday life suggests the need to ensure young students know about AI, its uses and limitations, and its benefits and risks, while enabling them to develop expertise in using AI-driven technologies. To support rural middle grades students and educators in learning and teaching AI concepts, we are designing AI-focused learning activities centered around the creation of digital gameplay experiences. To inform our designs, we conducted educator interviews and student focus groups to gain insights into their understanding of AI, their computer science background, and their knowledge and interest in gaming. Building on findings from these interviews and focus groups, we have designed a set of hands-on activities to elicit deeper feedback from students and educators on their preferences, points of confusion, and interests. In this work, we present our initial AI-infused game design activities.

Digital storytelling, which combines traditional storytelling with digital tools, has seen growing popularity as a means of creating motivating problem-solving activities in K-12 education. Though an attractive potential solution to integrating language arts skills across topic areas such as computational thinking and science, better understanding of how to structure and support these activities is needed to increase adoption by teachers. Building on prior research on block-based programming for interactive storytelling, we present initial results from a study of 28 narrative programs created by upper elementary students that were collected in both classroom and extracurricular contexts. The narrative programs are evaluated across multiple dimensions to better understand the types of narrative programs being created by the students, characteristics of the students who created the narratives, and what types of support could most benefit the students in their narrative program construction. In addition to analyzing the student-created narrative programs, we also provide recommendations for promising system-generated and instructor-led supports.