用大模型辅助编程，让学生专注理解NLP概念而非写代码。
From Code-Centric to Concept-Centric: Teaching NLP with LLM-Assisted "Vibe Coding"
- 用大模型生成代码，学生重点完成概念反思题。
- 19名学生满意度超4.4分，调试负担减轻，概念掌握更深入。
- 适合想培养批判性思维的NLP学习者，需配合提示词记录与反思评估。
大型语言模型（LLMs）的快速发展为自然语言处理（NLP）教育带来挑战与机遇。本文提出“Vibe Coding”教学法，利用LLM作为编码助手，同时聚焦概念理解与批判性思维培养。在一门高年级本科生NLP课程中，学生通过7个实验任务使用LLM生成代码，评估以批判性反思问题为主。对19名学生的课程反馈分析显示，其在参与度、概念学习和评分公平性方面的平均满意度达4.4–4.6/5.0。学生特别认可调试负担减轻带来的概念深度聚焦。但亦面临时间不足、LLM输出验证困难及任务说明不清等挑战。研究发现，只要通过强制提示词记录和反思式评估进行结构化设计，基于LLM的学习可实现从语法熟练到概念精通的转变，有助于学生适应人工智能增强的职业环境。
原文摘要 · Abstract (English)
The rapid advancement of Large Language Models (LLMs) presents both challenges and opportunities for Natural Language Processing (NLP) education. This paper introduces ``Vibe Coding,'' a pedagogical approach that leverages LLMs as coding assistants while maintaining focus on conceptual understanding and critical thinking. We describe the implementation of this approach in a senior-level undergraduate NLP course, where students completed seven labs using LLMs for code generation while being assessed primarily on conceptual understanding through critical reflection questions. Analysis of end-of-course feedback from 19 students reveals high satisfaction (mean scores 4.4-4.6/5.0) across engagement, conceptual learning, and assessment fairness. Students particularly valued the reduced cognitive load from debugging, enabling deeper focus on NLP concepts. However, challenges emerged around time constraints, LLM output verification, and the need for clearer task specifications. Our findings suggest that when properly structured with mandatory prompt logging and reflection-based assessment, LLM-assisted learning can shift focus from syntactic fluency to conceptual mastery, preparing students for an AI-augmented professional landscape.
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