标题： 大规模教学：利用人工智能评估和提升工程教育 显示英文标题

标题： Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education

摘要： 评估大规模教学效果仍然是大型大学持续面临的挑战，尤其是在招收数万名学生的工程专业中。 传统方法，如人工审查学生评价，通常不切实际，导致被忽视的见解和不一致的数据使用。 本文介绍了一种可扩展的、人工智能支持的框架，用于利用大型语言模型综合定性学生反馈。 该系统采用分层摘要、匿名化和异常处理，从开放式评论中提取可操作的主题，同时保持伦理保障。 可视化分析通过基于百分位数的比较、历史趋势和教学负荷来阐明数值分数。 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，结合学生、同行和自我反思输入，而不自动化人员决策。 我们报告了其在大型工程学院的成功部署。 通过与人工评审员的比较、教师反馈和纵向分析进行初步验证表明，LLM生成的摘要可以可靠地支持形成性评估和专业发展。 这项工作展示了如何设计具有透明度和共同治理的人工智能系统，可以在学术机构中大规模促进教学卓越和持续改进。 显示英文摘要

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.