Pre-uniform measures in the artificial intelligence era

Psychological measures allow researchers and psychological professionals to capture and quantify the latent attributes of human beings. Artificial intelligence (AI) techniques have been integrated into measurement practices to handle massive computational loads, automate repetitive procedures, and o...

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Vydáno v:Current psychology (New Brunswick, N.J.) Ročník 44; číslo 9; s. 7919 - 7933
Hlavní autor: Dong, Yixiao
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.05.2025
Springer Nature B.V
Témata:
Age groups
Algorithms
Artificial intelligence
Behavioral Science and Psychology
Large language models
Machine learning
Personality
Philosophy
Psychology
Quantitative psychology
Research methodology
Social Sciences
Validity
Pre-uniform measures
Metaheuristics algorithms
Large language models
Artificial intelligence
Machine learning
Measurement solution
ISSN:1046-1310, 1936-4733
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Shrnutí:Psychological measures allow researchers and psychological professionals to capture and quantify the latent attributes of human beings. Artificial intelligence (AI) techniques have been integrated into measurement practices to handle massive computational loads, automate repetitive procedures, and optimize decision-making based on psychometric evidence. The emerging applications of AI may inspire positive shifts in measurement norms. This study suggests a shift towards focusing on the format of developed measures or measurement products. Traditionally, measurement tools comprise a set of items selected by test developers based on previous studies, intended for uniform use unless rigorous revalidation is performed for scale modifications. This research highlights the challenges of using conventional uniform measures and proposes using pre-uniform measures with AI-derived applications to create optimized measurement solutions tailored to individual needs. It presents three illustrative examples involving various psychological constructs (i.e., creative activity engagement, exposure to racism, and openness personality trait) in different measurement contexts. Each example demonstrates how to apply a pre-uniform measure with an AI-derived method—such as metaheuristic algorithms, machine learning regularization, or large language models—to address a specific measurement objective or need. Furthermore, this work summarized a four-step implementation framework and discussed practical implications and future directions for advancing pre-uniform measures using AI applications.
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ISSN:1046-1310
1936-4733
DOI:10.1007/s12144-025-07374-1