Enhancing Learning Efficiency and Ergonomic Well-Being: A Comparative Study of Handwritten, AI-Assisted, and Digital Structured Note-Taking

Abstract

In an intense and demanding academic setting, note-taking is a core part of learning, where students must concentrate for long periods of time. While traditional handwritten notes are usually associated with deeper learning, newer digital and AI-assisted tools are increasingly used to reduce effort and improve efficiency. From a human factor perspective, it is a bit difficult to compare these different note-taking methods when both learning outcomes and workload are considered together. Most existing studies focus on either learning performance or how technology works, making it difficult to understand the trade-offs when we switch between different methods. This study looks at how handwritten, stylus-based digital, and AI-assisted note-taking methods affect learning retention and perceived cognitive workload using a within-subjects pilot study(N=11). Learning performance was measured using immediate and delayed retention quizzes, and workload was evaluated using the NASA Task Load Index (NASA-TLX). The results show no significant differences in immediate recall. Otter.Ai, an AI-assisted notetaking tool, has higher learning retention than the stylus-based condition for delayed recall (F2, 20 = 4.30, p = 0.028), while the handwritten method didn’t differ significantly from any other. Significant effects were also observed in physical demand (F2, 20 = 6.85, p = 0.005), effort (F2, 20 = 7.25, p = 0.004) and in frustration (F2, 20 = 4.44, p = 0.025). Together, these results show a clear trade-off between learning and workload. The study emphasizes the need to evaluate and ensure the correct usage of technology to help learn deeply, not just the efficiency.

Keywords: Note-taking Methods, Cognitive Workload, Learning Retention, AI-assisted Tools, NASA-TLX, Human Factors

DOI: 10.54941/ahfe1007975