CourseAssist：面向计算机科学教育的教学法适宜型AI辅导系统
CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education
AI总结：
本文提出CourseAssist，一个专为计算机科学教育定制的基于LLM的辅导系统，通过检索增强生成、用户意图分类和问题分解确保教学法适宜性，评估和部署表明其显著优于GPT-4基线并有效改善了学生学习体验。
摘要：
计算机科学课程注册人数的增长和班级规模的扩大，需要可扩展的自动化辅导解决方案来充分支持学生学习。尽管像GPT-4这样的大型语言模型（LLMs）已通过问答展现出辅助学生的潜力，但教育工作者对学生过度依赖、对生成代码的误解以及答案不准确的风险表示担忧。我们主张采取建设性方法，利用AI能力同时降低潜在风险，而非直接禁止这些工具。本海报介绍了CourseAssist，一个专为计算机科学教育定制的基于LLM的新型辅导系统。与通用LLM系统不同，CourseAssist使用检索增强生成、用户意图分类和问题分解，使AI响应与特定课程材料和学习目标保持一致，从而确保LLMs在教育环境中的教学法适宜性。我们使用来自一门编程语言课程的50个问答对数据集，以有用性、准确性和教学法适宜性为标准，将CourseAssist与GPT-4基线进行了评估。评估结果表明，CourseAssist显著优于基线，展现出其作为有效学习助手的潜力。我们还在一所大型公立R1研究型大学的6门计算机科学课程中部署了CourseAssist，覆盖超过500名学生。对20名学生用户的访谈表明，CourseAssist通过提高特定课程辅导帮助的可及性并缩短编程作业的反馈周期，改善了计算机科学教学。未来的工作将包括在更多大学进行广泛的试点测试，并探索学生、教育者和AI之间更好的协作关系，以改善计算机科学学习体验。
英文摘要：
The growing enrollments in computer science courses and increase in class sizes necessitate scalable, automated tutoring solutions to adequately support student learning. While Large Language Models (LLMs) like GPT-4 have demonstrated potential in assisting students through question-answering, educators express concerns over student overreliance, miscomprehension of generated code, and the risk of inaccurate answers. Rather than banning these tools outright, we advocate for a constructive approach that harnesses the capabilities of AI while mitigating potential risks. This poster introduces CourseAssist, a novel LLM-based tutoring system tailored for computer science education. Unlike generic LLM systems, CourseAssist uses retrieval-augmented generation, user intent classification, and question decomposition to align AI responses with specific course materials and learning objectives, thereby ensuring pedagogical appropriateness of LLMs in educational settings. We evaluated CourseAssist against a baseline of GPT-4 using a dataset of 50 question-answer pairs from a programming languages course, focusing on the criteria of usefulness, accuracy, and pedagogical appropriateness. Evaluation results show that CourseAssist significantly outperforms the baseline, demonstrating its potential to serve as an effective learning assistant. We have also deployed CourseAssist in 6 computer science courses at a large public R1 research university reaching over 500 students. Interviews with 20 student users show that CourseAssist improves computer science instruction by increasing the accessibility of course-specific tutoring help and shortening the feedback loop on their programming assignments. Future work will include extensive pilot testing at more universities and exploring better collaborative relationships between students, educators, and AI that improve computer science learning experiences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
