Objectives : Computer science has profoundly shaped contemporary life, not only through technological artifacts but also through the ways it frames questions, problems, and solutions. One prominent example is computational thinking, which is often presented as a general problem-solving competence for the digital age. This article examines what kinds of problems such framings imply and whether they are adequate when designing technological interventions that affect social and political contexts. More specifically, it investigates how the concept of problem is understood within Computer Science Education Research (CSER) and seeks to explain why certain understandings have become dominant. Study Method : We first develop a conceptual framework by outlining historical and theoretical perspectives on the concept of problem , informed by conceptual history, philosophy of science, Critical Theory, planning theory, and computational thinking. Building on this framework, we conduct a systematic scoping review of how problems and problem solving are discussed in the CSER literature, using a corpus of 85 articles from ICER, Koli Calling, ITiCSE, and TOCE. Findings : Our analysis suggests that CSER tends to emphasize structured, solution-oriented, and comparatively tame problems, while more open-ended, contested, and wicked problems remain marginal. We also find that contextualization and real-world framing are often used primarily to motivate or legitimize tasks, rather than to foster critical reflection on how problems are framed and what assumptions underlie them. Conclusions : We argue that the dominant problem framing in CSER is historically situated rather than natural or neutral. It is closely connected to broader social and economic commitments to formalization, optimization, and efficiency, including the logic of capitalist production. This has important implications for how computing research and education understand legitimacy, solvability, and intervention, and it points to the need for greater reflection on what counts as a problem and where the limits of computing-centered problem solving lie.
Curriculum design in K-12 computing education (CE) is often centered on core disciplinary ideas, principles, concepts and practices. However, their meaningful sequencing and interrelation within a coherent curricular progression has received comparatively little attention in the literature. Moreover, disciplinary concepts can acquire notably different meanings in the context of different applications. Both issues are made particularly apparent by recent technology innovations, particularly AI, which both introduces novel concepts and practices while also reframing established ones like program, model, or data. As a result, there is an increasing risk, especially but not only in the context of innovative technologies, that K-12 computing curricula become fragmented into seemingly independent content areas rather than coherently interrelating them in the progressive pursuit of meaningful mastery. In this conceptual article, I present a theoretical inquiry into the notion of curricular coherence in K-12 CE and propose a model to inform the coherent design of corresponding competences. Drawing on the notion of “fundamental idea,” developed primarily within the German CE community, and positioning it in the context of competence and curriculum design theory, I argue that fundamental ideas—understood as constitutive disciplinary goals—can provide a powerful basis to inform the coherence of curricular competence progressions. I illustrate the approach with the help of different examples. Moreover, I discuss how an idea-based curriculum can offer a more robust structure for the coherent and meaningful integration of innovative technologies, concepts, and skills.
This multidisciplinary study explores how children interact with a low-cost robotics educational tool designed to teach machine learning (ML) concepts through hands-on making. Artificial intelligence (AI) and ML are increasingly integrated into K-12 education, yet tangible approaches are not extensively studied. We conducted three researcher-led interventions with children aged 10, 11 and 13, each tasked to build an AI-enhanced robot using the robotics kit. Interventions were analyzed both qualitatively and with process mining methods to gain an in-depth view of the interactions. Findings indicate that designing and building were very central throughout the interventions, with the affordances guiding the design process forward. Children exhibited emerging data-driven thinking and ownership of their design process, while still requiring tailored scaffolding throughout the design process. We identify a general structure of the design process from all the interventions and emphasize the importance of building and designing activities. This study provides a new methodological lens to analyzing the interactions between children and embodied AI-educational tools while also contributing to the current body of knowledge on designing and effectively leveraging embodied learning tools.
Recent advancements in Large Language Models (LLMs) have had a substantial impact on software engineering research and practice, as well as on software engineering education. In Software Engineering (SE) education, prior studies have mainly examined students’ LLM usage through surveys, interviews, or short-term observations, offering limited visibility into their actual development activities. As a result, it remains unclear how students’ perceptions of LLM usage match their actual development practices. To address this gap, we analyze course project data collected over three years from 158 students enrolled in an upper-level SE course, together with students’ experience-sharing presentations, survey responses, and activity logs. We examine LLM integration by jointly analyzing students’ perceived usage and usefulness from surveys and presentations, their actual development activities captured through VS Code activity logs, and their project performance based on evaluation results across different levels of LLM support. By combining these analyses, we connect students’ reported LLM usage with their actual development activities and assess its relation to learning outcomes in SE course projects. Our results show that students utilized LLMs frequently for code-related tasks and generally found them helpful, with frequent usage increasing over the course project while perceptions shifted as project complexity grew. Activity log analysis revealed divergent patterns in LLM usage, reflecting both increasing dependency and deliberate efforts to maintain development independence. Across three years, integrating LLM support did not lead to statistically significant differences in final project achievement as measured by requirement satisfaction, although students with LLM support produced substantially larger software systems and lower-performing teams showed mild benefits, suggesting that LLMs may help reduce the influence of programming proficiency on project outcomes. Our findings highlight both the benefits and tensions of LLM integration in SE course projects and suggest that future SE course designs may incorporate more complex and challenging SE tasks beyond implementation-focused requirements, while also providing explicit education on prompt engineering, supporting students’ code comprehension of rapidly generated LLM-assisted code, and helping students avoid excessive dependency on LLM tools during development.
A critical challenge in Computer Science Higher Education (CSHE) is gender disparity. Despite ongoing efforts, gender disparity remains a crucial challenge in CSHE, affecting student engagement and learning outcomes. We wanted to investigate CSHE educators’ views on gender inclusivity or lack thereof, key factors influencing inclusivity in CSHE settings, and how various student facets, characteristics and behaviours, as perceived by education, influence different gender student participation and engagement. We used semi-structured interviews (n=20) with a range of experienced CSHE educators to explore these factors in depth. We report on our key findings, recommendations and future research directions.
