Lookee: Gaze Tracking-based Infant Vocabulary Comprehension Assessment and Analysis

Measuring preverbal vocabulary comprehension of young children is vital for early intervention and developmental evaluation, yet challenging due to their limited communication abilities. We introduce Lookee, an AI-powered vocabulary comprehension assessment tool through gaze tracking for toddlers in the preverbal stage. Lookee incorporates the Intermodal Preferential Looking Paradigm (IPLP), which is one of the prominent word comprehension measures for toddlers and estimates word comprehension through a random forest model analysis. We design and validate Lookee through user studies involving 19 toddlers and their parents. Then we identify necessary design requirements from potential stakeholders’ perspectives through in-depth interviews including researchers, clinicians, and parents. As a result, Lookee achieves considerable estimation accuracy with sufficient system usability and demonstrates key design requirements for each stakeholder group. From our study, we highlight necessary design implications in developing and validating AI-powered clinical tools for toddlers.