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.

Embedding curriculum content in relevant contexts is a challenge computer science teachers face. The idea of smart environments as networking embedded computers to complete everyday tasks provides strong possibilities for context orientation, including smart homes, smart cities, and Industry 4.0. In particular, the context smart home offers teachers exciting and versatile possibilities to introduce students to computer science. It enables teaching programming, networks, communication protocols, and electrical engineering in a student-oriented manner. In order to provide teachers with a low-barrier opportunity to use this context in computer science classes, we developed a smart-environments learning kit. The kit contains hardware that can be easily interconnected and recombined, leaving plenty of room for individual development and internal differentiation. First pilot uses of the kit in different learning groups revealed room for improvement: For instance, preparing the boards in computer science classes is too time-consuming, and the open-source firmware of the hardware modules is too complex to be covered in class. Following a design-based research approach, this work-in-progress paper documents lessons learned from the piloting implementations and illustrates potential solutions.

Research into students’ conceptions is inherently challenged with appropriately transferring profound research questions into suitable research instruments. This applies in particular, but not exclusively, to studies with primary school students. In order to investigate individuals’ conceptions, besides linear-linguistic questionnaire procedures, classically guided interviews are used, also triangulated with each other or further sub-instruments (e.g. concept mapping techniques). For this latter purpose, seek-and-find-drawings offer a good opportunity, both paper based and digital. This paper presents research on primary school students’ conceptions of computers in everyday objects and illustrates a methodical opportunity regarding the use of seek-and-find-drawings in Computer Science Education research. In the presented study, 60 primary school children were presented with a seek-and-find-drawing depicting several everyday encounter situations with computing systems. According to the results, primary school students understand not only personal computers, smartphones, tablets, video gaming consoles and (video) cameras, but in few cases also refrigerators, air conditioners and cars as systems containing (embedded) computers. The experience from the presented paper based hidden objects study can be used especially for future research projects.

In the two-semester course "Soft Skills and Technical Competence" at the University of Oldenburg, we support first-year computer science students in testing and applying the theoretical content from their first computer science lectures in a meaningful and practical context. The interdisciplinary lecture content is selected to be directly applied in the further course of studies and prepares the students for their first scientific work. Therefore, the course content includes creative methods for problem-solving and brainstorming, working in groups and projects, scientific writing and presenting with the help of slides and posters, and formulating (peer) feedback. In addition, we teach practical skills such as using (measuring) tools, reading and creating circuit diagrams and circuits, programming microcontrollers, 3D modeling, and soldering electrical components in order to accompany students holistically in the development cycle of their digital artifacts: namely from the first idea in their heads to the solution of an everyday problem to the finished prototype in their hands. The examination is a digital portfolio consisting of an individual web blog for assignments and a learning diary, a smart home group project, a group blog for the project documentation, and a final presentation. In this poster, we describe the structure and content of the course and give an overview of some improvements for the coming semesters.

It is a well known fact, that computer science (CS) is one of the most stereotyped and clichéd (STEM-)disciplines. Computer scientists are generally perceived as being male, physically unappealing, boring, uptight loners working with machines instead of people all day long. Obviously, these often unjustified stereotypes are not compatible with many high school graduates’ – especially girls’ and (young) women’s – self-concepts. Presumably, this incongruity is co-responsible for the low number of CS students in western cultures. In order to counteract these biases and clichés to broaden participation in CS study programs, these stereotypes need to be documented first. While this is already the case for CS and computer scientists in general, analogous investigations that focus on CS education (CSE) and CS teacher students in particular are still missing in scientific literature. Since western countries share a huge demand on qualified CS teachers, equivalent investigations can generate useful derivations for CSE study program planners as well. Therefore, this paper presents an online based repertory grid survey on the stereotyping of teacher students of various study subjects like CS, Physical Education, History, or Politics among others. Results show, that of all teacher candidates, CSE students were by far most likely to be rated unappealing and male. Attributed characteristics mostly were negatively connotated. At one of the survey’s questions focussing on who the participant would most likely ”like” (swipe right) and ”dislike” (swipe left) on the popular casual-dating-app TinderTM, the prototypical CSE student was ”disliked” by almost every second participant.

A basic principle of Computer Science is to break problems down into parts. Object Orientation, the paradigm Divide and Conquer, and Modularity are only three examples that make use of Part-Whole-Thinking, which is a fundamental skill enabling Life Long Learning. It includes the ability to cognitively (and often subconsciously) identify Part-Whole-Relationships, which help us to understand objects, systems, processes, definitions, and concepts. As part of a two-year Girls' Engineering course held at a Northern-German secondary school it was tried to improve students' overall understanding of Information Technology by including Part-Whole-Thinking into its course content. The way this was done was through the use of littleBits for rapid prototyping of various Internet-of-Things devices. In this short paper, this practical approach is presented and the advantages in explicitly making Part-Whole-Thinking a central topic in class are discussed.