As the growing energy demands for artificial intelligence collide with the limits of traditional computer chips, University of Missouri researchers are developing brain-inspired hardware that merges memory and processing — like neural synapses — to dramatically improve efficiency and enable more sustainable, energy-efficient AI.

A meta-analysis by Associate Professor Wang Zhe and PhD candidate Cheng Chao (East China Normal University) in Educational Psychology Review finds that seductive details have a small but significant negative effect on learning outcomes. Using three-level meta-analysis and MASEM, the study provides the first cross‑study evidence that extraneous cognitive load mediates this effect.