Evaluating cognitive workload in an AR environment using pupil area responses
Abstract
[EMBARGOED UNTIL 12/01/2026] This thesis investigates how augmented-reality (AR) instruction affects learners' cognitive workload, using pupil-area pupillometry synchronized with established subjective measures. Participants engaged with biomechanics-focused AR modules while wearing eye-tracking glasses, enabling continuous recording of their pupil responses during both learning and problem-solving phases. Introducing brief verbal nudges further modulates these dilation trajectories, suggesting that timely spoken prompts can help regulate mental effort. Together, the work delivers (i) a validated, non-intrusive protocol for assessing workload in head-worn AR; (ii) clear evidence that pupil dynamics differ distinctly between learning and problem-solving, underscoring the importance of phase-sensitive adaptation; (iii) proof that targeted verbal cues can influence cognitive engagement. These contributions deepen our understanding of cognitive processing in mixed-reality learning and lay the groundwork for workload-aware AR educational tools.
Table of Contents
PubMed ID
Degree
M.S.