🤖 AI Summary
This study addresses the challenge of insufficient personalized support in software engineering education, which hinders effective instruction in domain knowledge and modeling methodologies. To tackle this issue, the authors developed an intelligent tutoring system integrating a customized ChatGPT (GPT-3.5) with a course-specific knowledge base, deployed within a master’s-level course to support learning in cryptocurrency finance fundamentals and Domain-Driven Design (DDD). The system’s efficacy was validated through a five-dimensional evaluation—achieving 98.9% accuracy, 92.2% relevance, and 89.4% instructional value—and pre/post self-efficacy surveys, which revealed significant improvements in students’ confidence in applying domain knowledge and DDD principles. The project further distilled 17 reusable teaching practices, encompassing prompt engineering and curriculum integration strategies, offering a methodological innovation for incorporating generative AI into software engineering education.
📝 Abstract
Software engineering courses often require rapid upskilling in supporting knowledge areas such as domain understanding and modeling methods. We report an experience from a two-week milestone in a master's course where 29 students used a customized ChatGPT (GPT-3.5) tutor grounded in a curated course knowledge base to learn cryptocurrency-finance basics and Domain-Driven Design (DDD). We logged all interactions and evaluated a 34.5% random sample of prompt-answer pairs (60/~174) with a five-dimension rubric (accuracy, relevance, pedagogical value, cognitive load, supportiveness), and we collected pre/post self-efficacy. Responses were consistently accurate and relevant in this setting: accuracy averaged 98.9% with no factual errors and only 2/60 minor inaccuracies, and relevance averaged 92.2%. Pedagogical value was high (89.4%) with generally appropriate cognitive load (82.78%), but supportiveness was low (37.78%). Students reported large pre-post self-efficacy gains for genAI-assisted domain learning and DDD application. From these observations we distill seventeen concrete teaching practices spanning prompt/configuration and course/workflow design (e.g., setting expected granularity, constraining verbosity, curating guardrail examples, adding small credit with a simple quality rubric). Within this single-course context, results suggest that genAI-supported learning can complement instruction in domain understanding and modeling tasks, while leaving room to improve tone and follow-up structure.