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As AI systems increasingly make autonomous, goal-directed decisions, enabling young learners to understand how these systems reason and act is a central challenge in AI education. However, many introductory AI learning experiences treat AI as a black box, limiting students’ opportunities to reason about how AI systems represent goals, make decisions, and act in the world. This paper presents a narrative-centered game-based learning environment that makes AI planning visible as a form of goal-directed reasoning to support students’ emerging AI literacy. Within the learning environment, students engage in a story-driven investigation, collaborating with an AI-driven agent to solve consequential problems. Students construct and revise representations of goals and actions using a block-based interface, observe how an AI planner generates and executes plans in the game world, and iteratively refine their representations through a Use–Modify–Create scaffolding progression. The narrative context provides concrete stakes for planning decisions, while the behavior of the AI-driven agent serves as interpretable feedback that supports students’ sensemaking about goal-directed AI reasoning. We report findings from an implementation of the game with students ages 10--11, examining learning outcomes using pre- and post-assessments of AI knowledge. Results demonstrate statistically significant gains in students’ understanding of AI planning concepts related to goal representation and decision-making. These findings illustrate how narrative-centered game design can make goal-directed AI reasoning visible and foster young learners’ AI literacy. 

This paper explores the experiences and learning outcomes of preservice elementary teachers (PSTs) as they integrate computational thinking (CT) into their teaching practices during a structured field experience. Through a qualitative content analysis of video reflections from 27 PSTs, the study examines how teaching CT lessons to K-2 students enhances the PSTs’ understanding of CT and their pedagogical skills. The field experience, which involved hands-on activities using ScratchJr and Tale-Bot, revealed several key themes: the importance of hands-on learning for student engagement, the benefits of empowering students to take an active role in their learning, the necessity of balancing teacher guidance with student independence, and the development of PSTs’ confidence in implementing CT activities. The findings suggest that structured field experiences play a crucial role in preparing PSTs to effectively integrate CT into elementary education, bridging the gap between theoretical knowledge and practical application. The study emphasizes the need for teacher preparation programs to incorporate real-world teaching opportunities to foster PSTs' confidence and adaptability in teaching CT, thus equipping them to meet the demands of 21st-century classrooms. 

Within the field of K-2 CS education, unplugged computational thinking (CT) activities have been suggested as beneficial for younger students and shown to impact young students’ skills and motivation to learn about CS. This study sought to examine how children demonstrate CT competencies in unplugged sequencing tasks and how children use manipulatives to solve unplugged sequencing tasks. This case study approach examined two unplugged sequencing tasks for six children ranging from ages four to seven (pre-kindergarten to 2nd grade). Children showed evidence of several CT competencies during the sequencing tasks: (1) pattern recognition, (2) algorithms and procedures, (3) problem decomposition, and (4) debugging. The strategies and use of manipulatives to showcase CT competencies seemed to evolve in complexity based on age and developmental levels. Taking into account children’s abilities to demonstrate CT competencies, this study suggests that sequencing is a developmentally appropriate entry point for young children to begin engaging in other CT competencies. In addition, these unplugged sequencing tasks can also be easily integrated into other activities commonly experienced in early childhood classrooms. 

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.