Oportunidades para el uso de sistemas de IA en organizaciones de servicios sociales: desde la reducción de la rotación de personal hasta la asistencia en la extensión comunitaria Igor Shichkin 1 , Lyailya Khusnutdinova 2 , No’monjon To’rayev 3 , Olga Kiseleva 4 , Vasily Sinyukov 5 1 Plekhanov Russian University of Economics, Moscow, Russia. E-mail: shichkinia@mail.ru; ORCID ID: https://orcid.org/0000-0002-3158-0648. 2 Moscow Polytechnic University, Moscow, Russia. E-mail: Lavanda-55@mai.ru; ORCID ID: https://orcid.org/0000-0002-0095-1135. 3 International Islamic Academy of Uzbekistan, Tashkent, Uzbekistan. E-mail: n.torayev@mymail.academy; ORCID ID: https://orcid.org/0000-0003-1202-1581. 4 Moscow City University, Moscow, Russia. E-mail: kiselevaoi@mgpu.ru; ORCID ID: https://orcid.org/0000-0003-0816-8740. 5 Pacific National University, Khabarovsk, Russia. E-mail: v.sinukov@yandex.ru; ORCID ID: https://orcid.org/0000-0001-6266-0088. Resumen. El estudio tuvo como objetivo identificar las condiciones previas y las oportunidades para la introducción de algoritmos de redes neuronales para per- sonalizar la motivación no económica de los trabajadores sociales. Se prestó especial atención a los aspectos éticos y legales de la integración de la IA en la labor de los servicios sociales. La investigación siguió un diseño de métodos mixtos con una en- cuesta a expertos (n=24) y tres grupos focales con trabajadores sociales (n=18 en to- tal). El análisis cuantitativo de los datos se realizó mediante estadística no paramétrica (criterios de Kruskal-Wallis, Spearman y Wilcoxon), y el análisis cualitativo incluyó codificación temática. Los resultados obtenidos confirman el potencial de la IA para aumentar significativamente la satisfacción de los trabajadores de servicios sociales, pero solo bajo ciertas condiciones: protección de la privacidad de datos, participación de los empleados en la implementación de tecnologías y automatización de las tareas administrativas desde el inicio. El cumplimiento de estas condiciones garantiza la ética y la aceptabilidad legal de la integración de la IA, lo que facilita un aumento significativo de la eficiencia y la sostenibilidad de los servicios sociales. Palabras clave: trabajo social, motivación no financiera, burnout laboral, salarios arancelarios, riesgos éticos. Recibido: 13/03/2026 ~ Aceptado: 15/06/2026 INTERACCIÓN Y PERSPECTIVA Revista de Trabajo Social ISSN 2244-808X ~ Dep. Legal pp 201002Z43506 DOI: https://doi.org/10.5281/zenodo.21159627 Vol. 16 (3): 847 - 859 pp, 2026

848 Shichkin, Khusnutdinova, To’rayev, Kiseleva, Sinyukov Interacción y Perspectiva. Revista de Trabajo Social Vol. 16(3): 2026 Opportunities for the use of AI systems in social service organizations: from reducing staff turnover to assistance in community outreach Abstract. e study aimed to identify the preconditions and opportunities for the introduction of neural network algorithms to personalize the non-financial moti- vation of social workers. Particular attention was paid to the ethical and legal aspects of integrating AI into the work of social services. e research followed a mixed-meth- ods design with an expert survey (n=24) and three focus groups with social workers (total n=18). Quantitative data analysis was performed using non-parametric statis- tics (the Kruskal-Wallis, Spearman, and Wilcoxon criteria), and qualitative analysis involved thematic coding. e obtained results confirm the potential of AI to signifi- cantly increase the satisfaction of social service workers, but only under a number of conditions: data privacy protection, employees’ participation in the implementation of technologies, and the automation of administrative tasks from the outset. e satis- faction of these conditions secures the ethics and legal acceptability of AI integration, facilitating a significant increase in the efficiency and sustainability of social services. Key words: social work, non-financial motivation, occupational burnout, tariff wages, ethical risks. INTRODUCTION Staff turnover in social services reaches 30-40% in the first five years of work, and occu- pational burnout is recognized as a key factor in the intention to leave the profession (Győri & Ádám, 2024; Roulston et al., 2025). is problem is especially acute in social service organiza- tions ‒ institutions providing social services to the elderly, persons with disabilities, families with children, and other population categories. e specifics of work in these organizations aggravate the problem of staff motivation, as most employees are paid according to the tariff wage system, which limits opportunities for material incentives, and limited budgets cause the need for ad- ditional staff retention methods (Fedchenko et al., 2025). Artificial intelligence is being actively introduced into the work of social services to solve various problems (Abdullaev et al., 2023). Neural networks predict customer needs, automate risk assessments, optimize resource allocation, and support decisions on the issues of vulnerable population groups (Begishev et al., 2024; Grudtsina et al., 2025). One of the most promising, albeit understudied, areas is the application of AI in personnel management to solve the problem of staff turnover and occupational burnout (Vaslavskiy & Gabuev, 2017). e personalization of non-financial motivation with neural networks opens up new opportunities: the automation of routine administrative tasks can free up time to work with real people (Polozhentseva et al., 2024), burnout risk forecasting enables timely support, and the personalization of development programs improves job satisfaction (AIHR, 2025). However, the use of AI to motivate social service workers carries serious ethical risks. Algo- rithm bias can cause unfair evaluations of employees from different social groups, the collection

Oportunidades para el uso de sistemas de IA en organizaciones de servicios sociales 849 Vol. 16(3) septiembre - diciembre 2026/ 847 - 859 of data on emotional well-being and professional challenges threatens confidentiality (Severin, 2023), and automated systems can be perceived by staff as a control tool, ultimately undermin- ing trust instead of strengthening it (Arjanto et al., 2025; Balcombe, 2025; Szondy et al., 2025). Although current research on AI in HR and the ethical standards of social work is developing in parallel, these branches are not integrated well enough with respect to personalizing non- financial motivation (Taj et al., 2022; Saeidnia et al., 2024). e research goal in the present study was to analyze opportunities for the use of neural networks to personalize the non-financial motivation of social workers and to identify key AI technologies, ethical risks, and conditions for the successful implementation of AI in social ser- vices organizations. LITERATURE REVIEW Studies highlight seven key components of non-financial motivation for social workers: the recognition and evaluation of their work, quality supervision and feedback, opportunities for professional growth, autonomy in decision-making, perceived importance of work, work-life balance, and support from colleagues and organizational culture (Kim & Stoner, 2008; Zhang, 2023). Kim and Stoner (2008) have shown that work autonomy and social support reduce burnout and staff turnover. e ability of social workers to take care of their own well-being also plays an important part in retaining specialists (Zhang, 2023). e individual needs of em- ployees in these forms of support vary, which necessitates a personalized approach to motivation programs. Neural networks are able to personalize staff motivation in several aspects. In particular, predictive analytics can forecast turnover risk by analyzing behavioral patterns and employee engagement (AIHR, 2025). Early identification of such workers makes it possible to offer them timely support (Chikersal et al., 2020). AI can also create individual training programs, taking into account the competencies and psychological characteristics of each specialist ( Shabalina et al., 2024; Zaitseva & Vaslavskiy, 2025). Monitoring of emotional well-being helps identify early signs of burnout (Aziyev et al., 2024; Alimova et al., 2025). Automated systems are capable of generating career development recommendations based on employees’ profes- sional achievements and preferences. AI chatbots for basic staff support are achieving progress in personalizing interaction, al- though their effectiveness remains in question (Hom et al., 2017). Large language models auto- mate motivational interviewing, but are still inferior to humans in emotional depth (Zaitseva, 2023; Mykhaylenko et al., 2024). Feedback analysis through processing text data sheds light on problem areas within the organization (Tashnichenko & Tregub, 2024). Dedicated compu- tational motivation models have been developed to design adaptive personalized interventions (Taj et al., 2022). In social service organizations specifically, these technologies can free manag- ers from routine administrative tasks (Pavlova, 2024). However, the use of AI in the management of social service workers is fraught with several ethical risks. e collection of data on employees’ emotional state and professional challenges poses a threat to privacy, especially when the data can be leaked or misused (Dronova et al., 2025;

850 Shichkin, Khusnutdinova, To’rayev, Kiseleva, Sinyukov Interacción y Perspectiva. Revista de Trabajo Social Vol. 16(3): 2026 Lei, 2025). A systemic problem with AI in personnel assessment is bias, as large language models can retain implicit “hidden values” from training data that may not meet users’ cultural norms (Hadar-Shoval et al., 2023) and exacerbate inequality (Abdullaev, 2023; Smirnova, 2023). In turn, the automation of certain processes can reduce human contact and empathy, which are the backbone of organizational culture in social service organizations (Sapfirova et al., 2025). Confidentiality, transparency, fairness, and accountability are the key ethical principles es- sential for the responsible use of AI (Saeidnia et al., 2024; Minbaleev et al., 2025). Successful strategies take into account the cultural context and preserve human connections (Arjanto et al., 2025), balancing technological efficiency and the human component (Szondy et al., 2025). Research gaps and contributions of the study Our literature review has uncovered three key gaps in research. e first is the lack of re- search integrating the technological capabilities of neural networks in personalizing non-finan- cial with the ethical principles of social work. Most studies either focus on the technical aspects of AI (Taj et al., 2022; Mykhaylenko et al., 2024) or consider only the ethical issues (Reamer, 2023; Saeidnia et al., 2024). Second, there is not enough data on the long-term effectiveness of AI-based personalized motivational programs for personnel retention and burnout preven- tion in social work, as research on chatbots and motivational interview systems predominantly focuses on short-term outcomes (Mykhaylenko et al., 2024). ird, no practical recommenda- tions have been developed for the implementation of AI in social service organizations, which are distinguished by limited resources, the need to maintain a person-oriented approach, and the peculiarities of working with vulnerable groups (Reamer, 2023). us, the present study aimed to systematize the components of non-financial motivation in conditions of limited financial incentives, evaluate the applicability of specific AI technolo- gies for personalization, and propose a gradual implementation strategy that accommodates the specifics of social work with vulnerable population groups. METHODS e study employed a mixed methodology combining qualitative and quantitative meth- ods of data collection and analysis. Expert survey e structured survey was conducted with 24 specialists: heads of HR services in social service organizations (n=12), developers of AI solutions for HR (n=7), and experts in the field of professional ethics of social work (n=5). e selection criteria included at least 5 years of profes- sional experience, experience in the implementation or development of automation systems in HR, and having relevant scientific publications or practical projects. e survey was conducted online in October-November 2024 in the form of a questionnaire with 35 questions grouped into four blocks: (1) non-financial motivation factors; (2) the technological capabilities of neu- ral networks; (3) ethical risks; and (4) implementation recommendations. e questionnaire used both closed-ended questions with a 1-5 Likert scale and open-ended questions for qualita- tive data analysis.

Oportunidades para el uso de sistemas de IA en organizaciones de servicios sociales 851 Vol. 16(3) septiembre - diciembre 2026/ 847 - 859 Focus groups ree focus groups were organized with social workers (n=18, 6 people each) from social service organizations in three regions of Russia. e selection criteria included 2 to 15 years of work experience, age in the range of 25-55, and diverse experience in using digital tools. Discus- sions lasting 90-120 minutes were held in November 2024 and followed a semi-structured guide with a focus on the perception of non-financial motivation, attitudes towards AI in personnel motivation programs, and concerns about confidentiality and automation. e discussions were recorded with the participants’ consent and transcribed for analysis. Data analysis e quantitative data were processed by non-parametric methods: the Kruskal-Wallis test to compare the estimates of expert groups, Spearman’s rank correlation coefficient to identify re- lationships, and the Wilcoxon signed-rank test for paired comparisons. Non-parametric meth- ods were chosen due to the small sample size and abnormal data distribution. e qualitative data from open-ended questions and focus group transcripts were analyzed by thematic coding. Statistical processing was performed in IBM SPSS Statistics 26.0 with the significance level set at p<0.05. Ethical aspects All participants gave informed consent to participate in the study, data confidentiality and anonymity were guaranteed, and their participation was voluntary with the right to leave at any stage. e study also received the approval of the local ethics committee. RESULTS e results of the expert survey and focus groups are presented according to the four the- matic blocks of the study. e importance of various non-financial components of motivation for retaining person- nel in social work was assessed by experts on a 5-point Likert scale (Table 1). All components were rated highly. No statistically significant differences were found be- tween the expert groups (p>0.05), indicating consensus on key motivation factors. Spearman correlation analysis revealed significant relationships between “quality supervi- sion” and “recognition of work” and between “work-life balance” and “autonomy.” Next, the experts assessed the applicability of various AI technologies in personalizing non- financial motivation (Table 2).

852 Shichkin, Khusnutdinova, To’rayev, Kiseleva, Sinyukov Interacción y Perspectiva. Revista de Trabajo Social Vol. 16(3): 2026 TABLE 1. Expert assessment of the significance of non-financial motivation components (n=24) Non-financial motivation component HR managers (n=12) Me [IQR] AI developers (n=7) Me [IQR] Ethics experts (n=5) Me [IQR] Total sample Me [IQR] H (p)* Recognition and evaluation of work 5 [5-5] 4 [4-5] 5 [5-5] 5 [4-5] 3.21 (0.201) Quality supervision and feedback 5 [5-5] 4 [4-5] 5 [4-5] 5 [4-5] 4.89 (0.087) Professional growth opportunities 5 [4-5] 5 [4-5] 4 [4-5] 5 [4-5] 0.67 (0.715) Autonomous decision-making 4 [4-5] 4 [3-5] 4 [4-5] 4 [4-5] 1.52 (0.468) Perceived value of work 5 [5-5] 5 [4-5] 5 [5-5] 5 [5-5] 2.14 (0.343) Work-life balance 4 [4-5] 4 [3-4] 4 [4-5] 4 [4-5] 3.78 (0.151) Peer support and organizational culture 5 [4-5] 4 [3-5] 5 [4-5] 4 [4-5] 3.01 (0.222) *Kruskal-Wallis test for comparisons between expert groups; Me – median, IQR – interquartile range. TABLE 2. Assessed applicability of AI technologies to personalize motivation (n=24) AI technology Applicability Me [IQR] Technological readiness Me [IQR] Ethical acceptability Me [IQR] rs (p)* Staff turnover forecasting 5 [4-5] 4 [4-5] 4 [3-4] 0.41* Personalized training programs 5 [4-5] 4 [4-5] 4 [4-5] 0.63** Monitoring of emotional well-being 4 [3-5] 4 [3-4] 3 [2-4] 0.71** Automated career guidance 4 [4-5] 4 [4-5] 4 [3-4] 0.58** Chatbots for basic employee support 4 [3-4] 4 [4-5] 4 [3-4] 0.45* Feedback analysis and identification of problem areas 5 [4-5] 4 [4-5] 4 [4-5] 0.52** *p<0.05; **p<0.01 (Spearman’s rank correlation coefficient); Me – median, IQR – interquartile range. e monitoring of emotional well-being received the lowest ethical acceptability scores with moderate applicability. Significant positive correlations between applicability and ethical acceptability were observed across all technologies, which suggests a relationship between tech- nological appropriateness and ethical considerations. Following this, the experts assessed the severity of various ethical risks of AI in personnel management in social service organizations (Table 3).

Oportunidades para el uso de sistemas de IA en organizaciones de servicios sociales 853 Vol. 16(3) septiembre - diciembre 2026/ 847 - 859 TABLE 3. Assessed severity of the ethical risks of AI (n=24) Ethical risk HR managers (n=12) Me [IQR] AI developers (n=7) Me [IQR] Ethics experts (n=5) Me [IQR] Total sample Me [IQR] H (p)* Violation of employee data confidentiality 5 [4-5] 4 [3-5] 5 [5-5] 5 [4-5] 5.87 (0.053) AI systems’ bias in employee assessment 4 [4-5] 4 [3-4] 5 [4-5] 4 [4-5] 6.12* (0.047) Reduced human contact and empathy 5 [4-5] 3 [3-4] 4 [4-5] 4 [4-5] 8.45* (0.015) Perception of the system as a control tool 5 [5-5] 4 [3-4] 5 [4-5] 5 [4-5] 7.34* (0.025) Lack of algorithm transparency 4 [4-5] 3 [3-4] 4 [4-5] 4 [3-5] 4.23 (0.121) Dependence on technology and loss of skills 4 [3-5] 3 [3-4] 4 [4-4] 4 [3-4] 4.67 (0.097) Unequal access to technology (digital divide) 4 [4-5] 4 [3-4] 4 [4-5] 4 [4-5] 1.56 (0.458) *Kruskal-Wallis test; *p < 0.05; Me – median, IQR – interquartile range. Statistically significant differences between the expert groups were observed with respect to three risks. AI developers systematically give lower estimates of the risks compared to HR man- agers and ethics experts. e results of the expert survey and the qualitative analysis of focus groups were used to identify priority conditions for the implementation of AI (Table 4). TABLE 4. Priority of conditions for the successful implementation of AI systems (n=24). Conditions of introduction Importance Me [IQR] Current readiness of organizations Me [IQR] Rank difference (W, p)* Training personnel to work with AI systems 5 [5-5] 2 [2-3] 78.0 (p < 0.001) Transparency of algorithms and explainability of decisions 5 [5-5] 2 [2-3] 78.0 (p < 0.001) Privacy and data protection safeguards 5 [5-5] 3 [2-3] 78.0 (p < 0.001) Employees’ involvement in the implementation process 5 [4-5] 3 [2-3] 76.5 (p < 0.001) Human oversight of automated solutions 5 [5-5] 3 [2-4] 78.0 (p < 0.001) Gradual introduction with pilot projects 4 [4-5] 3 [3-4] 68.0 (p < 0.001) Regular bias audit 5 [4-5] 2 [2-2] 78.0 (p < 0.001) Adaptation to the specifics of social work 5 [4-5] 2 [2-3] 78.0 (p < 0.001) *Wilcoxon signed-rank test for paired comparisons; Me – median, IQR – interquartile range.

854 Shichkin, Khusnutdinova, To’rayev, Kiseleva, Sinyukov Interacción y Perspectiva. Revista de Trabajo Social Vol. 16(3): 2026 All conditions received high importance scores with low current readiness of organizations. e differences between importance and readiness are statistically significant for all conditions. ematic analysis of focus group transcripts revealed key themes in the attitudes of social workers toward the use of AI (Table 5). TABLE 5. Key themes from focus groups with social workers (n=18). eme Frequency of mentions Example statements Tone Concerns about control and surveillance 16/18 “I’m afraid it will be used to assess our performance, not to help”; “How do you make sure that management doesn’t get the data?” Negative Need for reduced administrative burden 18/18 “If AI takes over reporting, we’ll have more time for clients”; “I get tired of paperwork more than people.” Positive Skepticism about AI understanding the specifics of the job 14/18 “An algorithm can’t understand that the client is in a difficult situation”; “Each case is unique; templates do not work.” Negative Interest in personalized development recommendations 15/18 “It would be helpful to get advice on training based on my tasks”; “I want to know what skills I should work on.” Positive Concerns about digital inequality 11/18 “Not all colleagues are computer liter- ate”; “It will be difficult for older em- ployees.” Negative Condition: mandatory data anonymity 17/18 “Only if full confidentiality is guaran- teed”; “Nobody should be able to see my personal data.” Neutral (conditional) Desire to preserve live communication with management 18/18 “Supervision must be provided by a real person”; “No computer can re- place the support of a colleague.” Negative to replacement All focus group participants highlighted the value of reducing administrative burden and the need to maintain live communication. Most also mentioned mandatory data anonymity as a critical prerequisite for them to accept the system. DISCUSSION Expert consensus on the key components of motivation (perceived importance of work, quality supervision, and recognition of work) gives social services clear guidelines for the al- location of their limited resources. As the tariff wage system does not allow financial incentives to serve as the main personnel retention tool, organizations should purposefully invest in these three components (Kim & Stoner, 2008). e agreement of HR managers, AI developers, and ethics experts (p > 0.05) shows that there is no conflict of interest in choosing automation pri- orities. All groups of experts recognize the importance of freeing up managers’ time for quality supervision and acknowledging employees’ achievements.

Oportunidades para el uso de sistemas de IA en organizaciones de servicios sociales 855 Vol. 16(3) septiembre - diciembre 2026/ 847 - 859 e relationship between quality supervision and the recognition of work is of great practi- cal importance for social services. As these components reinforce each other, managers can be advised to combine regular feedback with public recognition of employees’ achievements. e link between autonomy and work-life balance points to a specific direction for the use of AI, as the automation of routine tasks can give employees more autonomy in scheduling their work hours. is aspect is especially significant for social workers who interact with clients at home or on the road because for them, flexible hours are critical to preventing burnout. e connection between technological applicability and ethical acceptability dictates the strategy for implementing AI in social services: technologies cannot be implemented simply because it is technically possible. Emotional well-being monitoring received the lowest ethi- cal acceptability scores precisely because social workers perceive it as a control tool. For social service organizations, this means the need to abandon “comprehensive monitoring” in favor of voluntary self-assessment tools, meaning that employees need to control what data is collected and who has access to it. Without privacy safeguards, any AI-based personnel motivation system will be perceived as a threat, compromising employee trust (Okishev, 2024; Lei, 2025). e uncovered contradiction calls for a revision of the implementation strategy. Kim and Stoner (2008) have shown that trust acts as a protective factor against burnout. erefore, AI systems perceived as controlling can amplify the exact risks they are designed to minimize (Bes- korovaynaya et al., 2025; Sapfirova, 2025). e unanimity of our study participants on the need to reduce administrative burden while maintaining live communication points to a potential entry point — the automation of routine tasks, which does not require sensitive data and hence meets less resistance (Belikova, 2024). Statistically significant differences between the importance of implementation conditions and the readiness of organizations reveal a systemic problem. e largest gap was observed in bias audit. Low readiness is indicative of the lack of institutional mechanisms to uphold algorithmic justice. Unlike EU countries, where a legal foundation has been created by the GDPR (Pakshin, 2023), Russia has neither regulatory frameworks nor methodological tools (Markheim & Lukyanova, 2023; Tereshchenko et al., 2025). is supports the argument of Reamer (2023) on the need for professional standards for the use of AI in social work (Smolin & Palchikova, 2024). e results of the study suggest a gradual implementation strategy, starting with the au- tomation of administrative tasks that require no sensitive data to build trust and then moving to personalization (Mamedova et al., 2025; Temirkanova et al., 2025). Investments need to be allocated for training and adapting systems to the specifics of social work (Shichkin et al., 2024). ere is also a need to create accessible audit tools, possibly through a centralized service at the regional level. CONCLUSION e main barrier to the implementation of AI technologies is the perception of monitoring as a control tool, which calls for a gradual implementation strategy, starting with the automation of administrative tasks. e observed gap between the importance of implementation condi-

856 Shichkin, Khusnutdinova, To’rayev, Kiseleva, Sinyukov Interacción y Perspectiva. Revista de Trabajo Social Vol. 16(3): 2026 tions (staff training, bias audit, algorithm transparency) and the current readiness of organiza- tions stresses the need to create institutional mechanisms and methodological tools adapted to the limited resources of social service organizations. e scientific significance of the study consists in the integration of the technological capabilities of neural networks with the ethical principles of social work and the systematization of expert assessments in the context of the Rus- sian social service system, where the tariff wage system gives critical importance to non-financial motivation. e practical significance of the study lies in the substantiation of an implementa- tion strategy focused on reducing operational burden and building trust before personalizing motivation programs. e outlined strategy will be able to solve the problem of staff turnover in the social services sector without significant financial costs. Naturally, the present study has certain significant limitations. First, the cross-sectional design leaves long-term efficacy beyond the scope of the study, which is one of the gaps identi- fied in the literature review. Moreover, the collected data reflects the perception of the experts rather than the actual results of implementation. Second, the small sample size imposes limits on statistical generalization. ird, the lack of data on AI systems already implemented in Rus- sian organizations means that the discussion is based on hypothetical scenarios. Finally, social desirability in focus group responses was not controlled. BIBLIOGRAPHIC REFERENCES • Abdullaev, E. E. (2023). “Opportunities for mutual assimilation of labor Law regulations of the Russian Federation and the UAE in conditions of activation of migration flows”. Lo- bbying in the Legislative Process, 2 (1), 22-28. https://doi.org/10.33693/2782-7372-2023- 2-1-22-28. • Abdullaev, I., Prodanova, N., Ahmed, M. A., Joshi, G. P., & Cho, W. (2023). “Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom indus- tries”. Electronic Research Archive, 31 (8), 4443-4458. https://doi.org/10.3934/era.2023227 • AIHR (2025, May 29). 15 HR analytics case studies with business impact. Available: https:// www.aihr.com/blog/hr-analytics-case-studies/ • Alimova, M., Vaslavskaya, I., Benevolenskaya, N., Sokolitsyna, N., & Sokolitsyn, A. (2025). “Influence of managers’ emotional intelligence on the formation of management style in socially oriented organizations”. Interaccion y Perspectiva, 16 (1), 149-163. https:// doi.org/10.5281/zenodo.17641712. • Arjanto, P., Makulua, I. J., Sampe, P. D., Huliselan, N., & Ellis, R. (2025). “Augmenting human connection: A systematic review of artificial intelligence in counseling practices, ethics, and cultural adaptation”. Jurnal Bimbingan dan Konseling Pandohop, 6 (1), 1-15. • Aziyev, A., Shalgynbayeva, K., Aitysheva, A., & Alpyssov, A. (2024). “Impact of psycho- logical training on the development of professionally important qualities of an educatio- nal psychologist”. European Journal of Contemporary Education, 13 (1), 5-13. https://doi. org/10.13187/ejced.2024.1.5 • Balcombe, L. (2025). “AI chatbots in mental health care: Integrative review of challenges and solutions”. Preprints. https://doi.org/10.20944/preprints2025.1893.v1

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