用ChatGPT辅助算法教学，提升学生平均分16.5分
Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective
- TA用结构化提示让ChatGPT生成习题与反馈
- 实验组平均分高出16.5分，高阶内容掌握更好
- 适合想提升教学效率的高校教师与助教
将大语言模型（如ChatGPT）融入计算机科学教育，为数据结构与算法（DSA）等复杂课程带来变革潜力。本研究从助教视角出发，考察在结构化提示与人工监督下，使用ChatGPT-4o和ChatGPT o1作为辅助工具的效果。通过对照实验，比较传统助教教学与混合模式——助教结合AI生成练习、解释概念并提供反馈。结构化提示强调问题分解、现实场景和代码示例，确保支持个性化且避免过度依赖AI。结果显示，采用混合模式的学生平均分高出16.50分，尤其在高阶知识点表现更优。但需助教验证输出以弥补模型局限。该框架凸显了LLM的双重作用：提升助教效率，同时通过人工审核保障准确性，为教育中人机协作提供了可扩展方案。
原文摘要 · Abstract (English)
Integrating large language models (LLMs) like ChatGPT into computer science education offers transformative potential for complex courses such as data structures and algorithms (DSA). This study examines ChatGPT as a supplementary tool for teaching assistants (TAs), guided by structured prompts and human oversight, to enhance instruction and student outcomes. A controlled experiment compared traditional TA-led instruction with a hybrid approach where TAs used ChatGPT-4o and ChatGPT o1 to generate exercises, clarify concepts, and provide feedback. Structured prompts emphasized problem decomposition, real-world context, and code examples, enabling tailored support while mitigating over-reliance on AI. Results demonstrated the hybrid approach's efficacy, with students in the ChatGPT-assisted group scoring 16.50 points higher on average and excelling in advanced topics. However, ChatGPT's limitations necessitated TA verification. This framework highlights the dual role of LLMs: augmenting TA efficiency while ensuring accuracy through human oversight, offering a scalable solution for human-AI collaboration in education.
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