The omnipresence of AI in everyday life highlights the importance of integrating learning about AI into K-12 computer science (CS) curricula to prepare students for the responsible use of this technology. However, empirical research on AI-specific teaching and learning processes in compulsory CS education is still emerging, with limited findings on students’ learning difficulties. This paper presents findings from a participatory action research study on students’ learning difficulties in compulsory CS lessons about AI in Year 11. The first iteration intentionally foregrounded teachers’ perspectives through data from lesson reflection protocols and semi-structured interviews. To complement these perspectives, a post-instruction learning assessment was carried out. Using a grounded theory-informed analysis, we derived 4 core categories of AI-specific difficulties: (1) evaluating whether real-world systems use AI approaches, (2) distinguishing between knowledge-based and data-based approaches, (3) evaluating AI models, and (4) applying AI concepts to real-world examples. Our analysis further revealed possible reasons for these difficulties, including students’ preconceptions, the lack of adequate tools, and the complexity of real-world AI systems. In a final workshop, the findings were collaboratively discussed, and implications for addressing the difficulties in future lessons were formulated. The study’s findings support the advancement of AI education and offer a foundation for future research on AI-specific teaching and learning processes.

Debugging is a central yet challenging activity for novice programmers, and diagnosing students' debugging difficulties places high demands on teachers under time pressure. Research shows that especially novice teachers often struggle to diagnose such situations in a precise and meaningful way. Although simulation-based approaches have proven effective for fostering diagnostic skills in other domains, they remain underexplored in computer science (CS) teacher education, also lacking theory-driven structures for designing diagnostically rich debugging cases. % To this end this paper introduces the ECDM Framework, a conceptual framework for constructing and analyzing realistic diagnostic cases for debugging situations. It integrates four core dimensions, Error, Cause, Debugging process, and Motivational-affective trajectory, to capture essential aspects of authentic debugging situations while allowing controlled complexity. The paper illustrates how the framework can be applied and discusses its potential to support teachers' diagnostic skills in debugging.

In today's data-driven world, the importance of data literacy is paramount. However, software engineering education has not adequately addressed integrating comprehensive data science curricula, leaving students ill-equipped for the future of artificial intelligence (AI), which is built on the foundations of data science. This gap is exacerbated by the lack of tailored courses and the intimidating nature of existing tools for begin-ners. Consequently, students often miss out on essential skills like data cleanup, real-world application of machine learning (ML) algorithms, and the integration of big data in software products. This paper addresses these challenges by proposing a novel approach to applied data science for software engineering students. We argue for a shift from traditional algorithm-focused teaching to a curriculum emphasizing real-world problem-solving, lever-aging data science techniques. By empowering students to define and tackle their own data-driven projects, we aim to increase motivation, enhance data literacy, and instill a data-thinking mindset in future software engineers to prepare them for the AI world. Overall, this paper contributes to the advancement of software engineering education for young learners by offering a comprehensive framework, the data action educational framework (DAEF), and a data science toolkit that enables DAEF by empowering learners to create original data-driven mobile apps.

Pair programming is widely recognized as an effective educational tool in computer science that promotes collaborative learning and mirrors real-world work dynamics. However, communication breakdowns within pairs significantly challenge this learning process. In this study, we use eye-tracking data recorded during pair programming sessions to study communication dynamics between various pair programming roles across different student, expert, and mixed group cohorts containing 19 participants. By combining eye-tracking data analysis with focus group interviews and questionnaires, we provide insights into communication’s multifaceted nature in pair programming. Our findings highlight distinct eye-tracking patterns indicating changes in communication skills across group compositions, with participants prioritizing code exploration over communication, especially during challenging tasks. Further, students showed a preference for pairing with experts, emphasizing the importance of understanding group formation in pair programming scenarios. These insights emphasize the importance of understanding group dynamics and enhancing communication skills through pair programming for successful outcomes in computer science education.

Social media has become an integral part of the lives of young people, who, despite being regarded as digital natives, lack essential skills in terms of the reflective use of data, thus underscoring the potential of computing education to empower their data literacy. To this end, this paper presents InstaClone, an innovative educational tool for classrooms that allows students to engage with social media platforms within a secure learning environment. With an appearance and functionality resembling Instagram, InstaClone offers a lifelike learning environment in which students can generate data, which are then processed and visualized on integrated data analytics dashboards, facilitating the development of individual data literacy. A case study with year 9 and year 10 students in a K-12 computer science class demonstrated that InstaClone convincingly emulates the real platform and that students benefit from using the tool by developing a deeper understanding of the data collected by social media platforms and the underlying algorithms.

Given the ever-growing importance of artificial intelligence in our society and daily lives, everyone needs to learn about the core ideas and principles of this technology. While there is still a lack of empirical findings on the teaching and learning about AI in K-12 education, various teaching approaches and materials have been developed in recent years, and the topic is being introduced into K-12 computer science curricula. However, qualifying CS teachers to adequately teach this new field is a significant challenge, as they require extensive content knowledge as well as pedagogical content knowledge. In this paper, we describe the conditions and challenges and the resulting design of a professional development offer to prepare teachers for the introduction of AI into mandatory K-12 CS education in Bavaria (Germany). By designing a scalable PD program in a blended learning format and building on principles such as the "pedagogical double-decker", we successfully addressed challenges such as limited resources, a large number of teachers to be trained, and the significant heterogeneity of teachers' backgrounds. We also share the results of a formal evaluation and other lessons learned from the initial implementations, which contribute to the design of professional development for this pressing issue.

Debugging code is a central skill in learning to program. Nevertheless, debugging poses a major hurdle in the K12 classroom, as students are often rather helpless and rely on the teacher hurrying from one student-PC to the other. Despite this, debugging is an underrepresented topic in the classroom as well as in computer science education research, as only few studies, materials and concepts discuss the explicit teaching of debugging. According to the constructivist learning theory, teaching and developing concepts and materials for the classroom have to take learners' preexisting experience into account. Students' preexisting debugging experience is built through troubleshooting, where they frequently find and fix errors in their daily lives - before they learn to program - for example when repairing their bicycle or if “the internet” stops working. Debugging is a special case of general troubleshooting and shares common characteristics, such as the overall process or particular strategies. Thus, the aim of this study is to develop an instrument for assessing preexisting debugging experience in the form of a real-world escape room consisting of debugging-related troubleshooting tasks. This allows us to observe students' troubleshooting process, strategies, and overall behavior in a natural environment and thus assess preexisting debugging experience. To this end, a design-based research process was conducted and a real-world escape room consisting of various troubleshooting tasks was developed. Those tasks and the escape room setting provide an innovative methodological approach to study students troubleshooting behavior and assess their preexisting debugging experience.

Self-reliance in debugging is both an important skill and a major challenge in learning to program. Debugging is distinct from general programming skills and needs to be taught explicitly. Nevertheless, when it comes to teaching and learning debugging, there are surprisingly few studies and results. The aim of this qualitative study is to investigate how students and teachers cope with errors in the K12 classroom, which debugging skills are conveyed, and why teachers teach or do not teach certain debugging skills. Therefore, in a first step, we identify skills considered relevant for debugging by applying desk research. We particularly focus on skills considered relevant for novices. Building upon this, we analyze 12 interviews of German high-school teachers using structured qualitative content analysis. The results show that especially weaker students are often helpless and apply a trial-and-error approach for coping with programming errors. It turns out that compile-time errors pose a big hurdle for many students. Teachers are mostly rushing from one student PC to the other, trying to help. Regarding the teaching of debugging skills, teachers focus on heuristics for common bugs as well as some debugging strategies. No systematic process on how to tackle and cope with errors is conveyed by teachers. Furthermore, they do not employ explicit teaching lessons on debugging. Overall, teachers lack a systematic approach for teaching debugging, as there are only insufficient concepts and materials.