10 papers
Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs
Ko Watanabe, Shoya Ishimaru
Employees often struggle to identify ``who knows what,'' leading to organizational productivity losses. We investigate whether Large Language Models (LLMs) can infer individual dom…
EyeBrain: Left and Right Brain Lateralization Activity Classification Through Pupil Diameter and Fixation Duration
Ko Watanabe, Pooja Pol, Nicolas GroÃmann +2
The relationship between brain lateralization and cognitive functions is well-documented. The left hemisphere primarily handles tasks such as language and arithmetic, while the rig…
Empowering Vocabulary Learning Through Teaching AI: Using LLMs as a Student to Perform Learning by Teaching in Vocabulary Acquisition
Tokio Uchida, Ko Watanabe, Andrew Vargo +5
"Learning by Teaching (LbT)" helps learners deepen their understanding by explaining concepts to others, with questions playing a vital role in identifying knowledge gaps and reinf…
What Do We Get from Two-Way Fixed Effects Regressions? Implications from Numerical Equivalence
Shoya Ishimaru
This paper develops numerical and causal interpretations of two-way fixed effects (TWFE) regressions in settings with nonbinary, nonstaggered treatments and time-varying covariates…
Push or Light: Nudging Standing to Break Prolonged Sitting
Sohshi Yoshida, Ko Watanabe, Andreas Dengel +3
Prolonged sitting is a health risk leading to metabolic and cardiovascular diseases. To combat this, various "nudging" strategies encourage stand-ups. Behavior change triggers use…
Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy
Sahana Yadnakudige Subramanya, Ko Watanabe, Andreas Dengel +1
Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions wi…