Executive functions, which are fundamental to children’s cognitive and academic development, undergo significant maturation during early childhood. This highlights the importance of supporting these pivotal processes from a young age. Recently, the development of serious games designed to train executive functions has become an active area of research. However, most studies have focused on children with deficits, such as those with attention-deficit/hyperactivity disorder, leaving the effects in typically developing children largely unexplored. In this work, eight serious games have been developed with the aim of training various executive functions within an academic setting. In a one-month pilot case series study, a group of 16 neurotypical primary school children who engaged with the games showed statistically significant improvements in working memory, inhibition, initiation, planning, task monitoring, and organization of materials, along with a reduction in parental burden. These exploratory results suggest that serious games may represent a promising approach to support executive function development in neurotypical children, addressing a gap in the literature.

Approximately one in seven people worldwide is affected by a mental disorder, making early identification crucial for preventing symptom progression and chronicity. However, diagnosis remains challenging, with discrepancies between referring physicians’ initial assessments and specialist psychiatric diagnoses reported in approximately half of cases. In recent years, machine learning has shown promising results in mental health assessment, with supervised techniques such as decision trees, random forests, and support vector machines among the most widely used approaches. However, these methods require large and representative datasets to capture the substantial heterogeneity of mental disorders, which can be difficult to obtain due to stigma, disease severity, and limited access to mental healthcare. This work investigates the feasibility of identifying mental disorders using data exclusively from a non-psychiatric population. The underlying hypothesis is that this population exhibits greater homogeneity and can therefore be characterized using a variational autoencoder with a Gaussian latent space. The proposed approach uses reconstruction errors from the non-psychiatric training population as a reference, with substantially higher errors in new observations indicating a potential mental disorder. Results show that the proposed VAE achieves performance comparable to that of the three aforementioned supervised techniques in the two dataset used. For the first dataset, the accuracy, sensitivity, specificity and ROC-AUC of the VAE were 0.88, 0.90, 0.85 and 0.95. These performance measures were 0.83, 0.86, 0.80, and 0.88 for decision trees; 0.88, 0.91, 0.85 and 0.94 for random forest; and 0.88, 0.91, 0.85 and 0.95 for support vector machines. Similar results were obtained for the second dataset. Moreover, the progressive increase in reconstruction errors across populations may reflect greater deviations from the patterns observed in the non-psychiatric population in groups with greater clinical complexity, supporting its potential use as a screening tool.