software engineering trainers

Abstract: Large language models (LLMs) can improve task performance during learning, yet these gains do not necessarily reflect learners' ability to thereafter apply the knowledge and skills independently. We present AI Cosmos, an agentic infrastructure for situated learning that organizes learning around two complementary principles: scenario-based participation and cumulative assets of practice. AI Cosmos organizes learners and role-specialized agents within learning scenarios that integrate task-relevant knowledge and, when needed, executable workbenches. An asset-memory loop preserves the processes and outcomes of practice as reusable assets with explicit provenance and dependencies. We evaluate scenario-based interaction through two preregistered randomized controlled studies examining the acquisition of legal reasoning and programming competence, holding the same underlying LLM across conditions. AI Cosmos achieves greater learning gains than a conventional LLM in legal reasoning and higher mastery of unseen programming problems than an LLM-as-Teacher baseline.

Abstract: The rapid adoption of Large Language Models (LLMs) is reshaping software engineering education, but their role in secure software engineering education remains underexplored. We report an exploratory empirical study of how 26 graduate students in a part-time MSc Cybersecurity programme used ChatGPT during a vulnerability-fixing assignment. To characterise ChatGPT use, we analysed students' ChatGPT interaction logs using a structured double-coding procedure and examined whether usage patterns and prior cybersecurity expertise were associated with assignment performance. The results show that students with varying levels of cybersecurity expertise used broadly similar ChatGPT strategies. Individual usage patterns showed descriptive differences by grade, but none remained statistically significant after correcting for multiple comparisons. In contrast, diversity of ChatGPT usage, i.e., the number of distinct usage patterns adopted, was positively associated with performance, even after controlling for cybersecurity expertise. These exploratory findings suggest that the way students engage with ChatGPT may be more informative than whether they use it, and motivate future controlled studies to guide students toward effective LLM use in secure software engineering education.