Intelligence in Motion: Integrating Test Engineering and Longitudinal Modeling for Sustainable Cognitive Assessment

This study aims to contribute to Sustainable Development Goal 4 by addressing two urgent gaps in cognitive assessment practice: unequal access to assessment and to the information it can provide. Access remains constrained by unequal student-to-professional ratios, while the breadth of information obtained depends partly on the resources available for more extensive assessment procedures. Static and dynamic traditions have addressed these challenges from different directions. Advances in test engineering have enabled self-administered multidomain cognitive batteries that reduce administration demands while dynamic approaches have shown weaker associations with situational and socioeconomic factors. We propose integrating these complementary strengths within a single process, using rapidly repeatable cognitive tests to preserve a broad static measure while generating short-interval trajectories examined through longitudinal modeling. The approach was tested in 428 students aged 12–16 years across ten administrations over 7–10 days. Longitudinal modeling characterized individual trajectories, while predictive-validity analyses showed that the mean across administrations explained up to 3.2 times as much variance in school achievement as the initial score. These findings suggest a route toward cognitive assessment that is less dependent on one-to-one professional administration while providing broader information about students’ performance level and change across repeated opportunities.