Generative Artificial Intelligence (AI) has demonstrated significant value in code generation and support for programming tasks, leading to its widespread adoption in both industry and academia. This proliferation has introduced new opportunities and risks for learning, teaching, and the broader landscape of computer science education. Despite this rapid integration, there remains limited understanding of how students engage with these tools, particularly in terms of collaboration, dependence, and delegation. More critically, little is known about how students regulate their interactions with AI tools over time, and how such processes contribute to problem-solving. In this case study, we coded 2,376 interactions from 120 undergraduate students with ChatGPT in a web programming course using a theoretically grounded self-regulated learning (SRL) in problem-solving coding scheme. To move beyond static counts and capture the temporal and structural dynamics of these interactions, we employed novel process-oriented learning analytics: transition network analysis and Sequence Analysis. Our findings reveal that while students frequently employed regulatory prompts aimed at process monitoring and problem-solving, they rarely engaged in deeper metacognitive strategies such as reflection or evaluation. This suggests a prevailing focus on surface-level regulation over deeper learning processes in student-AI interactions. Our results serve as a critical warning, highlighting a tendency towards 'cognitive offloading' that could undermine the development of independent, lifelong learners in computer science.
Neurodiversity remains underrepresented in higher education, especially in computing, where inclusive design is seldom embedded in curricula. We report an empirical study of a participatory inclusive IT design course at a German university of applied sciences. Twelve IT students collaborated with a neurodivergent peer expert and the university's accessibility advisor for students with disabilities and chronic health conditions to co-create prototypes addressing concrete barriers in everyday academic life. A mixed-methods evaluation combined entry and exit student surveys with a multi-stakeholder focus group. We find a statistically significant gain in self-reported knowledge of accessible design, alongside positive but non-significant shifts in attitudes and confidence, consistent with small samples and high pre-course endorsement. Qualitatively, participants described conditions that made participation workable, and stakeholders perceived the prototypes as valuable for neurodivergent students and potentially for students of all neurotypes. The study offers empirically grounded recommendations to embed participatory, inclusive practice in computing education.
We report two iterations (2023 and 2024) of a service-learning course in inclusive and accessible computing. Students co-designed with people with disabilities, including participants with complex support needs. Using a mixed-methods design with four measurement points per cohort, we combined validated survey scales with longitudinal, semi-structured interviews. The 2023 offering had limited field contact; the 2024 offering embedded four on-site co-design sessions across the term. Quantitatively, group means on global scales changed little from pre-contact to end of term, consistent with high baselines and elective enrollment, with small gains in self-efficacy. Qualitatively, repeated contact supported more empathetic framings, clearer evidence-anchored design rationales, and growing practical competence, with these narratives voiced more frequently in the higher-contact offering. Descriptively, the elective attracted a comparatively diverse set of students and a baseline profile consistent with reflective, prosocial orientations. We discuss implications for structuring sustained contact and scaffolding co-design in accessibility- and inclusion-oriented service-learning, and we argue that mixed-methods evaluation is essential to capture proximal, course-specific developments that global instruments may not detect over a single semester.
Security competitions in computing education model real-world crowd-sourced security (e.g., bug bounty programs), which has grown dramatically in recent years and has become the main source of software security reviews for many companies. This study explores practices and dynamics of high-performing teams of high school computing students in a K-12 computer security competition. While existing literature covers dynamics of security teams in structured organizations and student team formation for software engineering projects, additional insights are to be gained from this setting where students in this study a) self-selected into remote or hybrid teams, and b) developed collaborative strategies that enabled them to outperform thousands of teams on computer security tasks. We aim to fill this gap by addressing the following research questions regarding high-performing teams of high school computing students collaborating on computer security tasks: (1) How, why, and through which assembly mechanisms do teams form? (2) What technological needs do teams have, and how do they address them? (3) How do teams scope and distribute tasks, roles, and responsibilities? (4) What motivations and limitations do these teams have? (5) What factors and strategies seem to contribute to teams' performance? We conducted focus groups with the top five teams (out of 18,201 participating teams) of a computer security Capture-the-Flag (CTF) competition, all consisting of high school students in the U.S. To analyze the qualitative data collected using a structured-interview format, we analyzed data using grounded theory to address the research questions.We find that the teams in our study adopted a set of strategies centered on specialties, which allowed them to reduce issues relating to dispersion, double work, and lack of previous collaboration. We also connect these results to prior frameworks to conceptualize crowd work, including how students organically adopted existing strategies for task decomposition, hierarchical/reputational organization, task assignment, and collaboration. This work has implications in both computer security education and distributed teamwork strategies developed among high school students pre-COVID19 pandemic, which is before remote work became more common among this age group. The teams in our study model how scaling and distributing security work among team members is feasible, productive and beneficial. In addition, we identify various areas for future work, such as issues of social identity in high-skilled crowd work environments.
Large language models (LLMs) are widely used in education, yet most initiatives use LLM-powered tools as black boxes. This demo introduces a browser-based tool that lets students use their own data to train tiny transformer models directly on their own devices, without any servers or installation required. By making training visible in real time, the tool enables hands-on experimentation with datasets, model sizes, pre-training, fine-tuning, and prompting, while ensuring accessibility and privacy. It reframes language models from tools to use into systems to build, probe, and critique-supporting AI literacy and critical reflection in K-12 education.
