Longitudinal surveys are a crucial component of behavioral research. Such surveys, however, face significant gaps in the data created by item and unit non-responses as well as semantic gaps resulting from questionnaires, assessed trends, and data collection methods evolving over time. Using 15 waves of vaccination surveys as a test-bed, we demonstrate how modern AI techniques can bridge both item and unit gaps, originating from non-response, and semantic gaps, originating from instrument evolution. We address these gaps through a two-component framework. We leverage LLM-generated semantic embeddings of survey questions to encode question meaning, enabling a Deep & Cross Network used for imputation to jointly model responses across item semantics, individual characteristics, and temporal dynamics. This structure directly addresses survey evolution by operating in learned semantic space. To overcome data scarcity, we use cluster-informed synthetic data generation via hierarchical prompting that produces synthetic responses preserving distributional properties and empirical cluster structure. Our approach achieves a strong improvement in semantic gap tasks and 80-90% synthetic data fidelity, providing practical solutions for evolving longitudinal studies.

This study presents the validation of the Greek translation of the Godspeed Questionnaire Series (GQS) through an interactive workshop. Data were collected from 94 participants who assessed seven different wearable robotic devices in person and online. The validation examined psychometric characteristics including internal consistency reliability and factor structure preservation. The Greek translation successfully adapted semantic differentials while achieving equivalent performance compared to the original English version. Cross-language equivalence was maintained across most dimensions. All subscales demonstrated significant discriminative ability between robotic devices, validating the Greek GQS as a reliable instrument for human-robot interaction research and user assessment in wearable robotics applications.