Artificial intelligence in psychodiagnostics: cognitive states in a digital educational environment

<p>The article discusses the task of building multimodal AI models for diagnosing the cognitive state of students (concentration, fatigue, stress) in digital educational environments. The necessity of transition from traditional methods of psychodiagnostics to automated systems based on natural language processing, computer vision and behavioral analysis is substantiated. A mathematical model based on the CNN-LSTM hybrid architecture with the adaptation of parameters to individual cognitive profiles is proposed. The structure of the model is described, recommendations for its construction and integration into the digital educational infrastructure are given. The problems of interpretability, privacy, and sustainability of such models, as well as the prospects for their application, are discussed.</p>

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Publication Details

Journal
Modelling and Data Analysis
Published
2025-09-22
DOI
https://doi.org/10.17759/mda.2025150303
Primary Topic
Economic and Technological Innovation
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial intelligence in psychodiagnostics: cognitive states in a digital educational environment

Modelling and Data Analysis
Economic and Technological Innovation
article

Artificial intelligence in psychodiagnostics: cognitive states in a digital educational environment

article en

Abstract

<p>The article discusses the task of building multimodal AI models for diagnosing the cognitive state of students (concentration, fatigue, stress) in digital educational environments. The necessity of transition from traditional methods of psychodiagnostics to automated systems based on natural language processing, computer vision and behavioral analysis is substantiated. A mathematical model based on the CNN-LSTM hybrid architecture with the adaptation of parameters to individual cognitive profiles is proposed. The structure of the model is described, recommendations for its construction and integration into the digital educational infrastructure are given. The problems of interpretability, privacy, and sustainability of such models, as well as the prospects for their application, are discussed.</p>

Modelling and Data AnalysisVol. 15(3)
Moscow State University of Psychology & Education (RU)
Industry, innovation and infrastructure
Openalex Percentile: Top 36%
Economic and Technological Innovation
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