This working group aims to develop a research-based AI concept inventory (AI CI) to assess the understanding of foundational AI concepts among non-experts. By identifying core concepts and common misconceptions through literature reviews, expert consultations, and iterative validation, the group will create a user-friendly assessment tool that can be used to capture snapshots of AI understanding, support benchmarking across contexts, and inform educational initiatives and policy. Designed for diverse non-expert audiences, including educators, students, and the general public, this tool can provide valuable insights into how AI knowledge evolves over time, contributing to the broader goal of promoting AI literacy in everyday contexts.

This work-in-progress paper presents the development of a Concept Inventory (CI) specifically designed for Computer Science (CS) teacher training programs which are based on a wide experience in teacher-training. CS Teachers often do not have a formal background in CS therefore the objective of this paper is to identify and effectively address the misconceptions prevalent in the field of CS education. This is achieved by the following three steps: 1. Definition of key CS concepts, 2. Developing of multiple-choice questions that incorporate distractors based on widespread misconceptions, and 3. A comprehensive validation process which additionally ensures the effectiveness and reliability of these questions. The resulting CI focuses on the idea of conceptual reconstruction, aiming to enhance teachers' understanding of fundamental CS principles. The development process is iterative, grounded in educational theory and practice. The approach to developing this inventory illustrated in this work-in-progress paper includes a detailed approach to the creation of assessment questions, leveraging existing literature and expert insights. The paper also discusses the future plans for the expansion and adaptation of the CI, emphasising its role in elevating the quality of CS instruction. This work aims to significantly enhance CS teacher education by improving conceptual clarity and understanding in the field.

To investigate and identify promising practices in equitable K-12 computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being conducted and the lack of equity-focused research.

Across the globe, Computer Science Education has grown tremendously over the past decade to teach primary and secondary students computing ideas and tools. From integrating computational thinking in disciplines to teaching computer science as a stand alone subject, models for teacher preparation range from one and done professional learning workshops to full certificate and licensure programs. The group will focus on providing a landscape of how CS teachers are prepared academically in various countries and make evidence-based recommendations for how teachers should be educated to develop knowledge and skill to teach computer sci- ence. The working group will also discuss how to develop these knowledge systems while promoting instruction that is equitable and centers students in the classroom. In addition, the working group will focus on new directions in computing education (such as, artificial intelligence and machine learning) and their implica- tions for teacher preparation. We will bring together a group of international computer science education scholars who have been engaged in teacher preparation. In addition to what knowledge teachers need to teach CS, we will also focus on how the field is preparing teachers to think critically about AI/ML and the role of computer science in the design of technology tools to achieve goals while mitigating potential societal harms.

Nowadays, the ability to code is becoming increasingly important in industry and is often mentioned as a crucial skill for future employment. In maker culture, the ability to program along with other technical skills often exceeds this limitation and also includes fun activities through personally fabricated projects. In this paper, we examine computational design projects that are intended to be 3D printed and could potentially introduce coding learning actions. To find computationally rich designs that are appealing to learners, we conducted data analysis on an online makerspace which exploits computational design tools for personal fabrication. We found the most liked designs of the online makerspace and examined which of them included programming features (iterations, conditional statements and functions/modules) and could foster computational thinking elements such as abstract and algorithmic thinking, pattern recognition, and decomposition. The most liked category of designs was Figures (of animals and popular animation characters among other things) which surprisingly often also included programming features. We discuss the implications of our research for technologists, academics and teachers who wish to include 3D modeling and printing in coding learning activities.

The introduction of programming in K-12 can also include elements of other STEAM (Science, Technology, Engineering, Arts, Mathematics) subjects like engineering design and mathematics. Parametric Design is a design paradigm which is often related to architecture. Emerging technologies like additive manufacturing (3D printing) and modern CAD (Computer Aided Design) tools have enabled manufacturing of complex parametric designs not only by professionals but hobbyists and youth as well. In this paper we present our empirical investigation on how parametric design and digital fabrication tools can support programming learning actions. To investigate the potential of parametric design in programming learning activities, we analyzed the code of parametric design projects from a popular online makerspace and workshops that took place in two digital fabrication labs and an informatics school lab. In total 45 students participated in our workshops and developed code to generate 3D models, while each participant had the opportunity to 3D print at least one artifact. We designed, implemented and evaluated the workshops using mixed qualitative methods to triangulate the data. The results showed that the parametric design tools that we used were well suited for participants to understand core principles of programming (like loops, conditions, variables, functions) and engineering design, bringing unique advantages to understanding programming concepts through the visualization of the generated 3D models. Moreover, the participants seem to have understood how to use programming concepts like decomposition and indentation to designate logical blocks in the parametric design code. However, in some cases more advanced concepts were understood only on a surface level with the participants using commands without a deep understanding of them. Therefore, we present some critical remarks on parametric design and digital fabrication in educational context and their integration with programming, engineering design and mathematics.