用大模型打造化学工程热力学课的师生双用助手，兼顾答疑与教学反思。
Stan: An LLM-based thermodynamics course assistant
- 基于讲座转录与教材索引构建双用途系统，支持问答与教学分析。
- 可定位问题、识别困惑点并记录教学妙招，形成可搜索的学期教学档案。
- 全程本地运行，用开源模型保障隐私与可复现性，适合教育技术研究者。
当前人工智能在教育中的应用多聚焦学生端工具（如聊天机器人、辅导系统、习题生成），而对支持教师的潜力仍被忽视。我们介绍 Stan——一个面向本科化学工程热力学课程的工具套件，其数据管道同时服务于学生和教师：通过讲座转录文本和结构化教材索引，为学生提供基于检索增强生成（RAG）的自然语言问答，精准返回章节页码；为教师则通过结构化分析流程，生成每节课摘要、识别学生困惑点、归档教学轶事与类比，形成可搜索的学期级教学记录，支持课程复盘与改进。所有模块（语音转文字、内容结构提取、交互式问答）均在本地硬件上运行，采用开源权重模型（Whisper large-v3, Llama~3.1 8B），不依赖云服务，确保成本可控、数据隐私与独立可复现。我们描述了部署7–80亿参数模型处理长讲座转录时遇到的实际挑战，包括上下文截断、输出分布双峰、模式漂移，并提出相应缓解策略。
原文摘要 · Abstract (English)
Discussions of AI in education focus predominantly on student-facing tools -- chatbots, tutors, and problem generators -- while the potential for the same infrastructure to support instructors remains largely unexplored. We describe Stan, a suite of tools for an undergraduate chemical engineering thermodynamics course built on a data pipeline that we develop and deploy in dual roles: serving students and supporting instructors from a shared foundation of lecture transcripts and a structured textbook index. On the student side, a retrieval-augmented generation (RAG) pipeline answers natural-language queries by extracting technical terms, matching them against the textbook index, and synthesizing grounded responses with specific chapter and page references. On the instructor side, the same transcript corpus is processed through structured analysis pipelines that produce per-lecture summaries, identify student questions and moments of confusion, and catalog the anecdotes and analogies used to motivate difficult material -- providing a searchable, semester-scale record of teaching that supports course reflection, reminders, and improvement. All components, including speech-to-text transcription, structured content extraction, and interactive query answering, run entirely on locally controlled hardware using open-weight models (Whisper large-v3, Llama~3.1 8B) with no dependence on cloud APIs, ensuring predictable costs, full data privacy, and reproducibility independent of third-party services. We describe the design, implementation, and practical failure modes encountered when deploying 7--8 billion parameter models for structured extraction over long lecture transcripts, including context truncation, bimodal output distributions, and schema drift, along with the mitigations that resolved them.
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