Training novices by experts is often costly and time-consuming. Alternatively, learning systems offer a scalable and automated alternative. However, learning systems offer another, yet underexplored advantage, over training with experts: Analyzing novices and providing personalized training. This study explores the use of synthetically generated images to improve novice image classification skills in a GeoGuessr-inspired classification task. By leveraging a counterfactual-based approach and synthetically generated personalized training data, we aim to enhance individual learning. In a controlled experiment where participants classify Google Street View images from four different cities, we compare the impact of personalized synthetic images against randomly assigned ones. Our findings indicate that personalized training improves classification accuracy, underscoring the potential of intelligent learning. These results highlight a promising direction for integrating synthetic data into adaptive training environments in game-like settings, paving the way for effective and personalized intelligent learning systems.
Goutier et al. (Thu,) studied this question.