Military Pedagogy and Psychology under the Conditions of the russian-Ukrainian War: Problems, Solutions and Prospects for Development
Bibliographic record
Abstract
The article is devoted to the research of the main challenges in military pedagogy and psychology in the context of the russian-Ukrainian war, which has been ongoing since 2014 and escalated in 2022, with an emphasis on foresight strategies for addressing them. The research focuses on three main areas: psychological support for the Armed Forces of Ukraine, preparing citizens for national resistance, and the social and psychological reintegration of veterans. The aim of the research is to develop a scientifically grounded foresight strategy for the advancement of military pedagogy and psychology under the conditions of the russian-Ukrainian war. This includes identifying key challenges and gaps in the system of training military educators and psychologists, justifying the need to establish an institutional framework for coordinating scientific, educational, and practical initiatives, and preparing recommendations for the establishment of a specialised department and research institution on military pedagogy and psychology within the structure of the National Academy of Educational Sciences of Ukraine.The methodological foundation includes an interdisciplinary approach, foresight methodology, content analysis of publications, as well as structural and functional and comparative analysis of the experience from NATO countries (the United States, Israel, Finland, Canada, Australia). The analysis demonstrates the effectiveness of integrating psychological and pedagogical technologies into national security systems. It is proposed to establish a Department and an Institute of Military Pedagogy and Psychology within the National Academy of Educational Sciences of Ukraine to coordinate research, develop the “Military Psychologist” professional standard, educational programmes, and rehabilitation technologies. The foresight analysis outlines baseline, optimistic, and crisis scenarios through to 2030, with the optimistic forecast anticipating a 20% reduction in cases of absence without leave (AWOL), high-quality student training, and a 15% decrease in requests for assistance.The authors highlight systemic shortcomings such as the high rate of absence without leave (80,000 cases in 2024), insufficient psychological training of personnel, and limited stress management competencies among commanders. It is noted that the basic general military training for 70,000 students, introduced under the Law No. 3633-IX from 2025, is hindered by a shortage of personnel and methodologies. The rehabilitation of 1.2 million veterans, including 500,000 combatants, is slowed by the lack of comprehensive programmes, as evidenced by 18,000 requests for psychological assistance in April 2024.The recommendations imply introducing adaptive educational programmes for basic general military training and involving international experts to adapt best practices in the sphere of stress management, resistance training, and veteran rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".