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.

PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training / de Chiusole, Debora; Spinoso, Matilde; Anselmi, Pasquale; Bacherini, Alice; Balboni, Giulia; Mazzoni, Noemi; Brancaccio, Andrea; Epifania, Ottavia M.; Orsoni, Matteo; Giovagnoli, Sara; Garofalo, Sara; Benassi, Mariagrazia; Robusto, Egidio; Stefanutti, Luca; Pierluigi, Irene. - In: BRAIN SCIENCES. - ISSN 2076-3425. - ELETTRONICO. - 14:2(2024), pp. 122.1-122.29. [10.3390/brainsci14020122]

PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training

Spinoso, Matilde;Orsoni, Matteo;Giovagnoli, Sara;Garofalo, Sara;Benassi, Mariagrazia;
2024

Abstract

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.
2024
PsycAssist: A Web-Based Artificial Intelligence System Designed for Adaptive Neuropsychological Assessment and Training / de Chiusole, Debora; Spinoso, Matilde; Anselmi, Pasquale; Bacherini, Alice; Balboni, Giulia; Mazzoni, Noemi; Brancaccio, Andrea; Epifania, Ottavia M.; Orsoni, Matteo; Giovagnoli, Sara; Garofalo, Sara; Benassi, Mariagrazia; Robusto, Egidio; Stefanutti, Luca; Pierluigi, Irene. - In: BRAIN SCIENCES. - ISSN 2076-3425. - ELETTRONICO. - 14:2(2024), pp. 122.1-122.29. [10.3390/brainsci14020122]
de Chiusole, Debora; Spinoso, Matilde; Anselmi, Pasquale; Bacherini, Alice; Balboni, Giulia; Mazzoni, Noemi; Brancaccio, Andrea; Epifania, Ottavia M.; Orsoni, Matteo; Giovagnoli, Sara; Garofalo, Sara; Benassi, Mariagrazia; Robusto, Egidio; Stefanutti, Luca; Pierluigi, Irene
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/954593
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