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Journal of Modern Foreign Psychology
2025. Vol. 14, no. 3, 115–127
doi:10.17759/jmfp.2025140310
ISSN: 2304-4977 (online)

Application of Artificial Intelligence and Machine Learning Methods in Psychological Assessment and Analysis of Children's Drawings: Research Review

 
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Abstract

The article is devoted to the application of artificial intelligence and machine learning methods in psychological diagnostics using drawing tests. The possibilities of modern digital technologies for automating the analysis of projective techniques, such as «Human Drawing», aimed at assessing the cognitive, emotional and social development, as well as the mental health of children and adolescents are considered. It is emphasized that the traditional interpretation of drawing tests requires high qualifications and is associated with the risk of subjectivity, while artificial intelligence and machine learning can increase the accuracy, reliability and scalability of the assessment process. The article analyzes foreign studies demonstrating the use of convolutional neural networks, large language models and multimodal approaches for processing drawings, including feature extraction, classification and forecasting of psychological states. Particular attention is paid to the validity and reliability of the tools, as well as the problem of systematic errors in machine learning models. A general description of artificial intelligence and machine learning methods as applied to image analysis and, in particular, children's drawings is provided. Examples of successful application of artificial intelligence for analysis of children's drawings in the context of assessment of post-traumatic stress, cognitive and motor skills are given. The study highlights the interdisciplinary nature of the approach, combining psychology and engineering sciences, and the prospects for further development of automated tools for psychological diagnostics.

General Information

Keywords: drawing test, projective technique, human drawing, psychodiagnostics, machine vision, artificial intelligence

Journal rubric: Developmental Psychology and Age-Related Psychology

Article type: review article

DOI: https://doi.org/10.17759/jmfp.2025140310

Funding. This publication was prepared as part of the research project of the MSUPE Development Program «Development and implementation of a hardware and software complex for diagnostics based on the «Human Drawing» test» as part of the implementation of the «Priority 2030» Program.

Received 29.06.2025

Revised 29.06.2025

Accepted

Published

For citation: Sorokova, M.G., Filippova, E.V., Bulygina, M.V., Alekseychuk, A.S. (2025). Application of Artificial Intelligence and Machine Learning Methods in Psychological Assessment and Analysis of Children's Drawings: Research Review. Journal of Modern Foreign Psychology, 14(3), 115–127. (In Russ.). https://doi.org/10.17759/jmfp.2025140310

© Sorokova M.G., Filippova E.V., Bulygina M.V., Alekseychuk A.S., 2025

License: CC BY-NC 4.0

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Information About the Authors

Marina G. Sorokova, Doctor of Education, Candidate of Science (Physics and Matematics), Associate Professor, Head of the Department of Digital Education, Head of Scientific and Practical Center for Comprehensive Support of Psychological Research "PsyDATA", Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-1000-6487, e-mail: sorokovamg@mgppu.ru

Elena V. Filippova, Candidate of Science (Psychology), Professor, the head of the Child and family psychotherapy chair, Psychologicalcounseling faculty, Senior researcher, scientific secretary of the Moscow State University of Psychology and Education, employee of the Psychological Consultation of the Moscow State University of Psychology and Education, member of the editorial board of the journal “Counseling Psychology and Psychotherapy”, member of the editorial board of the journal “Psychological Science and Education”., Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-1328-3041, e-mail: e.v.filippova@mail.ru

Maria V. Bulygina, Candidate of Science (Psychology), associate professor at the chair of child and family psychotherapy department of psychological counseling, Moscow State University of Psychology & Education, Head of the Psychological and Pedagogical Laboratory of the State Educational Institution Education Center No. 1840, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-4459-0914, e-mail: buluginamv@mgppu.ru

Andrey S. Alekseychuk, Candidate of Science (Physics and Matematics), Associate Professor, Department of Mathematical Cybernetics, Moscow Aviation Institute (National Research University) (MAI), Associate Professor of the Department of Digital Education, Moscow State University of Psychology and education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0003-4167-8347, e-mail: alexejchuk@gmail.com

Contribution of the authors

Marina G. Sorokova — research management, conducting the research, writing — original draft.
Elena V. Filippova — conducting the research, writing — original draft.
Maria V. Bulygina — conducting the research, writing — original draft.
Andrey S. Alekseychuk. — conducting the research, writing — review & editing.
All authors participated in the discussion of the results and approved the final text of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

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