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With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.

 

Identifying misconceptions in student programming solutions is an important step in evaluating their comprehension of fundamental programming concepts. While misconceptions are latent constructs that are hard to evaluate directly from student programs, logical errors can signal their existence in students’ understanding. Tracing multiple occurrences of related logical bugs over different problems can provide strong evidence of students’ misconceptions. This study presents preliminary results of utilizing an interpretable state-ofthe- art Abstract Syntax Tree-based embedding neural network to identify logical mistakes in student code. In this study, we show a proof-of-concept of the errors identified in student programs by classifying correct versus incorrect programs. Our preliminary results show that our framework is able to automatically identify misconceptions without designing and applying a detailed rubric. This approach shows promise for improving the quality of instruction in introductory programming courses by providing educators with a powerful tool that offers personalized feedback while enabling accurate modeling of student misconceptions. 

Digital learning environments are used frequently in K-12 classrooms. Such use can require skillful orchestration as teachers need to understand the affordances of the learning environment, sequence of activities, and when and how to intervene with students. Using a digital learning environment in a multidisciplinary classroom context makes the design of support materials for teachers and students even more essential. To design for effective teacher orchestration in the classroom, we created a comprehensive set of materials for our multidisciplinary digital learning environment. We employ the design-based intervention research framework to trace the contextual and practical iterations these materials underwent. Additionally, we provide next steps for our work and considerations for the broader community.

 

Recent years have seen growing awareness of the potential digital storytelling brings to creating engaging K-12 learning experiences. By fostering students’ interdisciplinary knowledge and skills, digital storytelling holds great promise for realizing positive impacts on student learning in language arts as well as STEM subjects. In parallel, researchers and practitioners increasingly acknowledge the importance of computational thinking in supporting K-12 students’ problem solving across subjects and grade levels, including science and elementary school. Integrating the unique affordances of digital storytelling and computational thinking offers significant potential; however, careful attention must be given to ensure students and teachers are properly supported and not overwhelmed. In this paper, we present our work on a narrative-centered learning environment that engages upper elementary students (ages 9 to 11) in computational thinking and physical science through the creation of interactive science narratives. Leveraging log data from a pilot study with 28 students using the learning environment, we analyze the narrative programs students created across multiple dimensions to better understand the nature of the resulting narratives. Furthermore, we examine automating this analysis using artificial intelligence techniques to support real-time adaptive feedback. Results indicate that the learning environment enabled students to create interactive digital stories demonstrating their understanding of physical science, computational thinking, and narrative concepts, while the automated assessment techniques showed promise for enabling real-time feedback and support. 

Digital storytelling in combination with makerspace activities holds significant potential to engage students and support their learning. When students play, such as through makerspace activities, they engage in critical thinking and problem solving. In our work, we are joining storytelling with computational thinking (CT) practices, physical science exploration, and makerspace activities through a digital narrative-centered learning environment for elementary school. Learning within the environment is undergirded by makerspace play that centers on finding solutions to an open problem—how can stranded scientists on a remote island power up their village using found materials? The learning environment supports students’ CT practices and science content learning as they use and problem solve with physical energy conversion kits, culminating in their creation of an interactive story. We present here a brief case study of the ways students’ experiences with makerspace play support their problem solving and storytelling.