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

TL;DR

This paper introduces a scalable AI framework using large language models to analyze and synthesize qualitative student feedback for large engineering programs, enabling effective teaching evaluation at scale.

Contribution

It presents a novel AI-supported system that automates qualitative feedback analysis with ethical safeguards, hierarchical summarization, and visual analytics, tailored for large-scale engineering education.

Findings

AI summaries reliably support formative evaluation.

System deployed successfully across a large engineering college.

Preliminary validation shows alignment with human review.

Abstract

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.