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Behavioral Sciences
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Applications of Artificial Intelligence in Cognitive Assessment
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Applications of Artificial Intelligence in Cognitive Assessment

A Special Issue of Behavioral Sciences (ISSN 2076-328X).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1565

Editor

Dr. Antonio Laverghetta Jr.

E-Mail Website
Guest Editor
Hazel Quantitative Analysis Center, Wesleyan University, Middletown, CT 06459, USA
Interests: natural language processing; computational creativity; psychometrics; automated item generation; automated scoring of cognitive assessments

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into cognitive assessment has great potential for enhancing our understanding of and ability to measure human behavior. AI methods, especially those based on generative AI and large language models (LLMs), can advance cognitive assessment by streamlining the development of new cognitive tests, automatically rating assessment data, or generating synthetic human behavioral data, among many other possible uses. However, AI also brings with it new challenges for the collection of cognitive assessment data, such as participants completing online assessments partially or entirely using AI, or adversarially manipulating automated scoring models to produce desired results. This Special Issue of Behavioral Sciences welcomes original research, review articles, conceptual and theoretical work, and applied research that focuses on the applications of artificial intelligence in human cognitive assessment. Areas of interest include, but are not limited to, automated item generation or test construction using AI, the integration of AI into measurement models, automated scoring of cognitive assessments, generative modeling of human behavior, item parameter estimation using AI, cognitive modeling using AI, and novel approaches for measuring cognitive constructs using AI. The Special Issue seeks to bring together diverse research that critically examines both the strengths and weaknesses of integrating AI into cognitive assessment.

Dr. Antonio Laverghetta Jr.
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Behavioral Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • cognitive assessment
  • large language models
  • automated scoring
  • automated item generation
  • generative AI
  • psychometrics
  • synthetic data generation
  • test development
  • measurement models

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Published Papers (2 papers)

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Research

18 pages, 519 KB  
Article
Deep Semantics Analysis for Scale Development: Replicating Psychometrics Validation Process with Large Language Models
by Nicola Milano, Rosa Pizzo, Cristiano Scandurra, Maria Francesca Freda and Davide Marocco
Behav. Sci. 2026, 16(8), 1338; https://doi.org/10.3390/bs16081338 - 4 Aug 2026
Viewed by 512
Abstract
The present study examines whether large language model (LLM) embeddings can approximate the psychometric validation process of a newly developed psychological scale. Using the Academic Psychological Distress Scale (APDS) as a case study, we replicated the full validation pipeline, traditionally performed on human [...] Read more.
The present study examines whether large language model (LLM) embeddings can approximate the psychometric validation process of a newly developed psychological scale. Using the Academic Psychological Distress Scale (APDS) as a case study, we replicated the full validation pipeline, traditionally performed on human response data, by applying exploratory and confirmatory factor analyses (EFA and CFA) to semantic similarity matrices derived from LLM embeddings. We first computed cosine similarity among all 58 APDS items and found a moderately strong correspondence with the participants’ correlation matrix (ρ = 0.57). An EFA performed on the embedding-based similarity matrix yielded a six-factor solution explaining 79.1% of the variance. The best-performing model (32 items, loading cutoff = 0.70) demonstrated acceptable model fit when tested on participants’ responses (CFI = 0.88; TLI = 0.87; RMSEA = 0.07–0.08; SRMR = 0.06) numerically comparable to the fit obtained in the original human-based validation based on 367 participants’ response. Reliability estimates were satisfactory for most factors (ω/α = 0.82–0.95), although one factor showed lower internal consistency. The percentage of overlap between human-derived and embedding-derived structures, indicated an overall structural correspondence of 64%, with factor-level overlap ranging from 50% to 100%. While embeddings reproduced most dimensions of academic distress, they failed to recover the demoralization factor and instead introduced an additional behavioral cluster. These findings suggest that LLM embeddings can capture substantial aspects of the latent structure of psychological constructs and can support early-stage, low-data, embedding-first scale development. We propose a linguistic validity pipeline as a methodological framework for integrating semantic embeddings into psychometric validation procedures, highlighting their promise as a complementary tool for human-centered assessment. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Cognitive Assessment)
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24 pages, 459 KB  
Article
The Double-Edged Sword Effect of Government Chatbot Empathy on Citizens’ Continued Usage Intention
by Xuesong Li, Yangying Zhou and Muqun Hu
Behav. Sci. 2026, 16(7), 1095; https://doi.org/10.3390/bs16071095 - 2 Jul 2026
Viewed by 472
Abstract
With recent breakthroughs in affective computing and AI algorithms, government chatbots are evolving from function-oriented tools to emotionally responsive agents capable of detecting, decoding, and responding to citizens’ emotional cues. This shift holds significant potential for enhancing citizens’ continued usage intention. Drawing on [...] Read more.
With recent breakthroughs in affective computing and AI algorithms, government chatbots are evolving from function-oriented tools to emotionally responsive agents capable of detecting, decoding, and responding to citizens’ emotional cues. This shift holds significant potential for enhancing citizens’ continued usage intention. Drawing on social presence theory and employing a scenario-based experimental design, this study investigates whether, how, and under what conditions empathy by government chatbots affects citizens’ willingness to continue using such services. The findings reveal that empathy positively influence continued usage intention by enhancing users’ psychological engagement and perceived information richness. However, the strength of this effect is significantly moderated by the type of time pressure. Specifically, under endogenous time pressure, the positive effect of government chatbot empathy is amplified, whereas under exogenous time pressure, the effect is attenuated. This study uncovers the double-edged nature of empathic design in government chatbots, contributing to the literature on human–robot interaction in public service contexts. It also offers practical implications for the adaptive design of empathic government chatbots to optimize citizen engagement and service effectiveness. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Cognitive Assessment)
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