用定制AI导师助学生两周掌握金融与建模知识，提升学习自信心。
From Domain Understanding to Design Readiness: a playbook for GenAI-supported learning in Software Engineering
- 用定制ChatGPT+课程知识库，引导学生学加密货币与领域驱动设计
- 回答准确率98.9%，相关性92.2%，自信心提升显著
- 适合教学设计者参考，优化AI提示与课程流程
在硕士课程的两周实践中，29名学生使用基于精选课程知识库的定制ChatGPT（GPT-3.5）导师，学习加密货币金融基础与领域驱动设计（DDD）。我们记录全部交互，对随机抽样的60组问答（占总样本约34.5%）进行五维评估（准确性、相关性、教学价值、认知负荷、支持性）。结果表明：准确性平均达98.9%（无事实错误，仅2例轻微偏差），相关性92.2%，教学价值89.4%，认知负荷适中（82.78%），但支持性偏低（37.78%）。学生自评显示，使用GenAI后在领域学习与DDD应用上的自信心显著提升。据此提炼出17项具体教学实践，涵盖提示设计、配置策略与课程流程优化（如设定粒度、限制冗长回复、精选防护示例、引入简单评分机制）。研究表明，在此单一课程背景下，GenAI可有效辅助领域理解与建模教学，但在语气与反馈结构上仍有改进空间。
原文摘要 · Abstract (English)
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.
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