This research examines why undergraduate engineering students struggle with troubleshooting technical problems. By observing electrical engineering students fixing broken circuits, he aims to identify where they get stuck, compare their approaches with expert strategies, and develop classroom exercises that build practical troubleshooting skills for labs, projects and real-world engineering work.

This research critiques AI-based classroom monitoring, arguing that while algorithms can measure behavior, they cannot interpret meaning. It proposes the “Augustinian limit,” where AI supports logistics but human judgment guides interpretation. The framework protects authentic learning moments, emphasizing that true education relies on human insight, not just data-driven evaluation.

This study explores how archival research enhances student engagement and writing in undergraduate courses. Interviews with writing instructors reveal that hands-on work with archival materials—especially local and diversity-focused collections—deepens curiosity, strengthens research skills, and enables students to produce meaningful, socially relevant original work, even within constrained course structures.