一句话：本文报告在软件工程硕士课程中使用定制 ChatGPT 导师学习领域驱动设计的经验，并提炼出 17 条教学实践。
软件工程课程常需学生快速掌握领域理解与建模方法。本文报告一项硕士课程两周里程碑经验：29 名学生使用基于精选知识库定制的 ChatGPT（GPT-3.5）导师，学习加密货币金融与领域驱动设计（DDD）。作者记录全部交互，以五维评分标准（准确性、相关性、教学价值、认知负荷、支持性）评估 34.5% 随机样本（60/约174 对提示-回答），并收集前后测自我效能感。结果：准确率 98.9%，相关性 92.2%，教学价值 89.4%，认知负荷适当 82.78%，但支持性仅 37.78%；学生自我效能感显著提升。作者提炼出 17 条教学实践，涵盖提示配置与课程设计。在此单课程情境下，GenAI 支持的学习可补充领域理解与建模教学，但语气与追问结构仍需改进。
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.