Teaching at Scale: Leveraging AI to Evaluate and Elevate Engineering Education
This work addresses the problem of scalable teaching evaluation for large universities, particularly in engineering education, by providing a practical AI tool that enhances data use and professional development, though it is incremental as it builds on existing qualitative analysis methods.
The paper tackles the challenge of evaluating teaching effectiveness at scale in large engineering programs by developing an AI-supported framework that uses large language models to synthesize qualitative student feedback, reporting successful deployment with preliminary validation showing LLM-generated summaries can reliably support formative evaluation.
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.