Computational_psychometrics
Computational psychometrics is an interdisciplinary field fusing theory-based psychometrics, learning and cognitive sciences, and data-driven AI-based computational models as applied to large-scale/high-dimensional learning, assessment,[1] biometric, or psychological data. Computational psychometrics is frequently concerned with providing actionable and meaningful feedback to individuals based on measurement and analysis of individual differences as they pertain to specific areas of enquiry.
The relatively recent availability of large-scale psychometric data in accessible formats, alongside the rapid increase in CPU processing power, widespread accessibility and application of cluster and cloud computing, and the development of increasingly sensitive instruments for collecting biometric information has allowed big-data analytical and computational methods to expand the scale and scope of traditional psychometric areas of enquiry and modeling.[citation needed]
Pursuing a computational approach to psychometrics often involves scientists working in multidisciplinary teams with expertise in artificial intelligence, machine learning, deep learning and neural network modeling, natural language processing, mathematics and statistics, developmental and cognitive psychology, computer science, data science, learning sciences, virtual and augmented reality, and traditional psychometrics. [citation needed]
Another important subfield of computational science and, specifically, AI is what has been called psychometric artificial intelligence (PAI). PAI involves the use of psychometrically developed evaluations, such as intelligence tests and thinking style tests, to be solved algorithmically by an artificial agent. The goal of PAI is to put to the test the design and processing mechanisms proposed by AI researchers in order to get knowledge from both artificial and natural cognitive systems.[2][3]