Assessing executive functions in individuals with disorders or clinical conditions can be challenging, as they may lack the abilities needed for conventional test formats. The use of more personalized test versions, such as adaptive assessments, might be helpful in evaluating individuals with specific needs. This paper introduces PsycAssist, a web-based artificial intelligence system designed for neuropsychological adaptive assessment and training. PsycAssist is a highly flexible and scalable system based on procedural knowledge space theory and may be used potentially with many types of tests. We present the architecture and adaptive assessment engine of PsycAssist and the two currently available tests: Adap-ToL, an adaptive version of the Tower of London-like test to assess planning skills, and MatriKS, a Raven-like test to evaluate fluid intelligence. Finally, we describe the results of an investigation of the usability of Adap-ToL and MatriKS: the evaluators perceived these tools as appropriate and well-suited for their intended purposes, and the test-takers perceived the assessment as a positive experience. To sum up, PsycAssist represents an innovative and promising tool to tailor evaluation and training to the specific characteristics of the individual, useful for clinical practice.

The rapid integration of digital tools in educational and clinical settings highlights the need for assessing their usability and acceptability, particularly among populations of developmental age. This study aims to validate the Usability and Acceptability Scale (UAS), a new scale tailored for children aged 4 to 18, derived from integration and an adaptation of the Technology Acceptance Model Scale and System Usability Scale. The UAS was administered to a sample of 908 participants. Results of Exploratory and Confirmatory Factor Analyses consistently supported a bifactorial model encompassing the two dimensions of usability and acceptability. Reliability was also assessed using Cronbach’s α and McDonald’s ω , with overall results indicating acceptable internal consistency. These findings suggested that the UAS is a valid and reliable questionnaire for evaluating digital tools among younger users, offering valuable insights for developers and educators aiming to create child-friendly technologies.

Cognitive profiling plays a crucial role in understanding learning dynamics, it contributes significantly to the development of students’ metacognitive skills and awareness of the learning process, thereby facilitating the adoption of tailored learning experiences. Clustering, proves effective in cognitive profiling. However, the challenge of the “curse of dimensionality” introduces complexities that can impact the accuracy of cluster subject attribution. This paper investigates the evaluation of various cluster internal validation metrics and cluster stability using a dataset of 1626 participants comprising 54 items across six cognitive domains from the digital assessment tool, PROFFILO. We employ three clustering procedures—K-means, Gaussian Mixture Models, and Fuzzy-C Means—on raw data and apply linear (Principal Component Analysis) or non-linear (Variational Autoencoders), or a combination of PCA and VAE dimensionality reduction techniques. Results indicate that, for high-dimensional cognitive domains, a combination of PCA and VAE yields superior clustering quality. Conversely, in less high-dimensional domains, the VAE outperforms the PCA approach. In summary, the application of dimensionality reduction techniques demonstrates promising outcomes in student cognitive profiling, especially for data characterized by high dimensionality and heterogeneity. These findings have practical implications for advancing personalized learning experiences and enhancing our understanding of the intricate relationships within students’ cognitive domains.

Conventional evaluations of matrix reasoning have focused predominantly on total accuracy scores, thus overlooking qualitative error patterns that may reflect distinct cognitive mechanisms in psychiatric populations. This study examined performance on MatriKS, a digital matrix reasoning task, in a sample of 28 individuals diagnosed with Schizophrenia, 31 individuals diagnosed with Bipolar disorder, and 371 healthy controls. The present study analysed four conceptual error types: repetition (R), wrong principle (WP), distractor (D), and incomplete correlate (IC). The results showed that both clinical groups exhibited a lower level of accuracy and a higher total number of errors when compared to the control group. However, these differences were not statistically significant. The qualitative error analysis revealed informative dissociations: D errors, which reflect the selection of visually salient but relationally irrelevant options, were found to be more prevalent in both clinical groups, with a tendency observed in individuals with Schizophrenia. Multinomial logistic regression with bootstrap resampling identified selective visual attention, measured by the Attentive Matrices Test, as the only significant cognitive predictor of reasoning errors. It was also found that higher visuospatial attentional capacity significantly reduced WP errors and showed a marginal protective effect against IC errors. Furthermore, the study revealed that there were no significant interactions between cognitive measures and diagnostic group. Symptom severity, as measured by the Brief Psychiatric Rating Scale, did not demonstrated correlations with total errors. The overall findings of this study underscore the clinical value of qualitative error profiling as a complement to total score metrics, and highlight selective visual attention as a key cognitive mechanism in matrix reasoning.