大规模教学：利用人工智能评估与提升工程教育
Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education
AI总结：
本文提出一个基于大型语言模型的可扩展AI框架，通过分层总结和可视化分析综合学生反馈，在大型工程学院成功部署，支持形成性评估与教学改进。
摘要：
对于大型大学而言，大规模评估教学有效性仍然是一个持续的挑战，尤其是在招收数万名学生的工程项目中。传统方法（如人工审阅学生评价）往往不切实际，导致洞察被忽视和数据使用不一致。本文提出了一种可扩展的、由人工智能支持的框架，利用大型语言模型综合定性学生反馈。该系统采用分层总结、匿名化和异常处理，从开放式评论中提取可操作的主题，同时维护伦理保障。可视化分析通过百分位比较、历史趋势和教学负荷来情境化数值评分。该方法支持有意义的评估，并符合定性分析和教育评估的最佳实践，整合了学生、同行和自我反思的输入，而不自动化人事决策。我们报告了其在大型工程学院的成功部署。通过与人工评审员、教师反馈和纵向分析的比较进行的初步验证表明，大型语言模型生成的摘要能够可靠地支持形成性评估和专业发展。这项工作展示了当人工智能系统以透明和共同治理为设计原则时，如何在学术机构内大规模促进教学卓越和持续改进。
英文摘要：
Evaluating teaching effectiveness at scale remains a persistent challenge for large universities, particularly within engineering programs that enroll tens of thousands of students. Traditional methods, such as manual review of student evaluations, are often impractical, leading to overlooked insights and inconsistent data use. This article presents a scalable, AI-supported framework for synthesizing qualitative student feedback using large language models. The system employs hierarchical summarization, anonymization, and exception handling to extract actionable themes from open-ended comments while upholding ethical safeguards. Visual analytics contextualize numeric scores through percentile-based comparisons, historical trends, and instructional load. The approach supports meaningful evaluation and aligns with best practices in qualitative analysis and educational assessment, incorporating student, peer, and self-reflective inputs without automating personnel decisions. We report on its successful deployment across a large college of engineering. Preliminary validation through comparisons with human reviewers, faculty feedback, and longitudinal analysis suggests that LLM-generated summaries can reliably support formative evaluation and professional development. This work demonstrates how AI systems, when designed with transparency and shared governance, can promote teaching excellence and continuous improvement at scale within academic institutions.
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。
