Psychological Measurement in the Information Age: Machine-Learned Computational Models

Abstract

Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.

Analysis of Psychological Shaping Function of Music Education under the Background of Artificial Intelligence

“…D'mello and others combined with the new requirements of talent training in colleges and universities; music education integrates intelligent system, which can e ectively optimize the overall development ability of professional courses, create a school running idea in line with music education, ensure the overall quality of music education, and try to fully implement the shaping of psychological function from the perspective of high starting point and high level [5]. Dresp Langley and others combined with the overall development of music education, and focused on the construction of students' practical ability [6].…”

“…Combined with the life elements of infants and young children, select the corresponding background music, effectively play music works, and form a music edification process. D'mello and others believe that during the sleep of infants and young children, music is used to adjust the mood of infants and young children, form a music atmosphere and shape the stable and stable psychological function of infants and young children [5]. At this time, the selected music is quiet and soft, which increases the infants' perception of music rhythm and imitates it with the rhythm.…”

Toward Argument‐Based Fairness with an Application to AI‐Enhanced Educational Assessments

“…Whereas the above proposed approach to argument-based fairness holds for both traditional and AI assessments, it is the stages of assessment, types of claims, and types of evidence that can separate the latter from the former, at a minimum. As such, it is important to first identify the unique features of AI assessments as discussed extensively elsewhere (D'Mello et al, 2021;Tay et al, 2022) and summarized below:…”

“…They, too, centered their approach to fairness on matters of bias only and aligned the bias concerns to stages of assessment development, which are somewhat different than traditional assessments as elaborated later and discussed in D'Mello et al. (2021).…”

“…Meanwhile, researchers and practitioners are increasingly developing educational and psychological assessments that utilize AI to automate and enhance assessment processes (von Davier et al., 2019; D'Mello et al., 2021). In alignment with the National Artificial Intelligence Initiative Act (2020), we define AI as a machine‐based system that can “make predictions, recommendations, or decisions influencing real or virtual environments” (p. 5), and thus our definition includes a variety of methods that may be used to achieve these actions, such as machine learning, neural networks, and natural language processing.…”