Personalized Model-Driven Adaptive Task Facilitates Visuomotor Skill Learning Mediated by Promoting Flow Experience

The ability to rapidly acquire novel visuomotor skills is essential for daily functioning tasks such as motor rehabilitation, surgical operation, and mechanical assembly. Previous research suggested that experiencing flow can enhance learning outcomes. Although dynamic difficulty adjustment (DDA) has been commonly used to induce flow and maximize engagement, most existing methods rely on model-free, stepwise adaptations that lack quantitative, model-based support. In this study, we proposed a personalized, model-driven DDA approach to facilitate visuomotor skill acquisition by enhancing flow during the learning process. We implemented an adaptive fine fingertip force control task with DDA based on optimal control principles to train the visuomotor skills. This DDA updated task difficultly using real-time multiple performance metrics, with parameters derived from an individually fitted model that captures each user's motor behavior during the task. A user study, involving two groups, compared the effects of a model-driven adaptive task with a model-free control task. Results from the flow state scale and physiological recordings demonstrated that the model-driven task elicited significantly higher levels of flow than the model-free task. Moreover, participants in the model-driven group showed a notably higher learning rate in visuomotor skills (19%) compared to the model-free group (8%). These findings underscore the potential of integrating personalized modeling and optimal control theory to optimize user experience and accelerate learning outcomes in DDA frameworks when building adaptive human–machine interaction systems.