This poster reports a qualitative study investigating pre-service computer science teachers' perspectives on artificial intelligence (AI) education in a Teaching--Learning Lab. Twelve pre-service teachers designed and implemented AI learning activities with secondary school students in this out-of-school learning environment. Semi-structured interviews explored how pre-service teachers perceive the role of this setting for AI-related learning. The analysis reconstructs a preliminary category system capturing how they frame the potentials, challenges, and educational role of Teaching--Learning Labs for AI education.

This study explores primary school children's ideas and curiosity about artificial intelligence (AI) using a combination of semi-structured interviews, a seek-and-find drawing task, and self-generated questions. The findings reveal six perspectives on AI, highlighting children's conceptions of AI as both a human-like entity with emotional and cognitive traits and a technological tool integrated into their daily lives. Their self-generated questions demonstrate curiosity about the practical, ethical, and philosophical dimensions of AI, often drawing parallels to human learning and interactions. By addressing a research gap in early AI education, this study provides insights for designing age-appropriate AI curricula that build on children's pre-instructional conceptions, fostering foundational understanding and equitable access to AI education.

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.

While Artificial Intelligence (AI) has long been a staple in Computer Science (CS) discourse, recent advancements in AI technologies have notably reshaped society and influenced the everyday lives of students. In this context, this paper investigates the conceptions of AI among school students in grades 7 to 10 in Germany within the Model of Educational Reconstruction, using drawing techniques and seek-and-find drawings in the field of CS Education Research (CSER). According to our findings, learners often hold conceptions about AI influenced by the media, frequently involving anthropomorphism, wherein human characteristics are attributed to AI. As a result, most of their drawings associate AI with robots having arms, legs, and a brain. Additionally, concrete technical objects, such as drawings of voice assistants like Siri and Alexa, are also perceived as AI. However, there are also some drawings that present more scientifically accurate conceptions. These insights underscore the necessity for CS Education to recognize and address these naive conceptions, fostering a more precise and scientifically grounded understanding of AI. Our study introduces a methodological approach to integrating drawings into the field of qualitative CSER and shares insights into valuable lessons learned regarding what worked well and what did not for future research projects.