Theoretical basis of the problem solved
Artificial intelligence (AI) represents one of the most significant technological innovations of today, which fundamentally changes the functioning of several areas of social life, including social work. The development of algorithmic systems, machine learning and automation creates new possibilities for more effective solutions to social problems, increasing the availability of services and supporting qualified decision-making in complex situations. At the same time, however, this technological transformation brings significant ethical and methodological challenges, especially in the areas of transparency of algorithms, fairness of decision-making processes, and responsibility for the impact of technologies on individuals and communities (Floridi et al., 2020; Jobin et al., 2021). As a result of rapid technological progress, the growth of the volume of digital data and the development of advanced algorithms, artificial intelligence has become one of the key pillars of the current digital transformation of society. Its influence goes beyond the technical level and significantly affects the organization of work, education and interpersonal interactions, thereby placing new demands on professional competences and ethical frameworks for the performance of helping professions (OECD, 2021; European Commission, 2022).
In this context, social work faces the fundamental challenge of adapting to the digital environment, which requires the ability of social workers to effectively use information technology, understand digital data and critically reflect on its use in practice. The integration of artificial intelligence represents one of the most significant shifts in this transformation, as AI has the potential to support risk assessment, intervention planning and administrative processes, but also raises issues of protecting the dignity, autonomy and privacy of clients (Boddy & Boddy, 2021; Eubanks, 2022). Despite the growing international interest in the digitization of social services, the question of the real use of artificial intelligence in the Slovak context remains insufficiently explored.
Digital transformation significantly affects the way social services are provided, while one of its key trends is the gradual integration of artificial intelligence into professional practice. Current professional literature emphasizes that the basic prerequisite for the meaningful and safe use of AI in social work is a sufficient level of digital and AI literacy of social workers, which enables them not only to use technologies, but also to understand their functioning and critically assess their limits. Ahn et al. (2025) point out that the successful integration of AI into social work is significantly conditioned by the level of the so-called "AI literacy", including the ability to interpret algorithmic outputs and responsibly incorporate them into decision-making processes without violating the ethical principles of the profession. At the same time, Turner-Lee and Du (2025) point out that there are significant differences between individual professional groups in the ability to effectively work with artificial intelligence technologies, while factors such as age, previous technological experience and technological confidence play a significant role. In addition to the individual characteristics of social workers, the literature also points to the importance of the wider work and regional context. Research indicates that the implementation of artificial intelligence is significantly influenced by organizational and infrastructural conditions that differ between urban and rural environments (Boetto, 2025). As a rule, urban environments have better digital infrastructure, a higher rate of innovation and a greater number of organizations using technological solutions, while rural regions may face technological limitations and a lack of systematic support for the introduction of innovations, which is reflected in a lower rate of use of artificial intelligence (Molala, 2024).
Research methodology
The theoretical framework of the presented research is primarily the theory of innovation adoption and the concept of technological readiness, which explain why individuals and professional groups adopt new technologies at different rates and at different times. According to Rogers' theory of innovation diffusion, the adoption of a technological innovation is conditioned not only by its objective properties, but also by the characteristics of users, their experiences, attitudes and the social context in which they operate. In the environment of social work, the character of the clientele and the nature of the work performed also play an important role, which influence the degree of technological need, the frequency of contact with digital tools and the willingness to experiment with new solutions. Work with younger age groups of clients is more often associated with digitized forms of communication, records and interventions, which can naturally support the development of experiences with AI. On the contrary, work with seniors is more rooted in traditional forms of personal contact and care, which can slow down the process of technological adoption. This theoretical approach makes it possible to interpret the differences in the experiences of social workers with artificial intelligence as a result of the interaction of individual competencies, the professional context and the age structure of the clientele, and thus creates a framework for empirical verification of the relationship between the age of clients and the rate of use of AI in social work.
The research study was conceived as a quantitative cross-sectional study aimed at identifying factors influencing the rate of use of artificial intelligence in the practice of social workers and analyzing the relationship between the age of the clientele with whom the workers work and their level of experience with the use of artificial intelligence in professional practice
The research tool consisted of a structured questionnaire consisting of blocks focused on demographic data, age structure of the clientele, level of digital literacy, work environment and the degree of use of artificial intelligence in professional practice. The questionnaire was distributed in electronic form, while the target population was social workers working in various types of social services. A purposeful selection was chosen, which made it possible to target social workers working in urban and rural environments, as well as in various areas of social work, with the aim of ensuring representation of work with clients across the entire age spectrum - from children and youth to seniors.
The obtained data were processed using descriptive statistics, which provided an overview of the age distribution of the research sample, the level of digital literacy, the field of work performance in terms of the age of the clientele and the level of experience with the use of artificial intelligence in professional practice. Inferential statistical methods were used to verify the formulated hypotheses. The relationship between the age of social workers, digital literacy and the use of artificial intelligence was analyzed using correlation indicators and regression analysis, which made it possible to estimate the probability of using AI based on the identified predictors. As part of this research, the relationship between the age of the clientele and the level of experience of social workers with the use of artificial intelligence was also analyzed, while non-parametric methods suitable for ordinal variables were used to identify the trend, namely Spearman's correlation, Kendall's tau-b test and the Kruskal–Wallis test to compare the differences between individual age groups of the clientele.
Aim and Hypotheses
The aim of the research was to analyze the influence of individual and contextual factors of social workers, including the age of the clientele, on the rate of use and experience with artificial intelligence in social work.
H1: If the age of social workers is higher and the level of digital literacy is lower, the probability of using artificial intelligence in practice decreases significantly.
H2: We assume that social workers working in urban environments use artificial intelligence in practice significantly more often than workers working in rural areas.
H3: We assume that social workers working with younger age groups of clients (children and youth) show a higher level of experience with the use of artificial intelligence compared to social workers working with older age groups of clients (adults and seniors).
Data Collection and Research Sample
The research was conducted in the months of July - October 2025 on a sample of 110 respondents. Women represented the unequivocal majority of respondents, namely 87 persons, while there were 23 men. The age structure of the research group was relatively diversified, with the largest group being respondents aged 40-49 (39 persons). This was followed by a group of social workers aged 30–39 (30 people) and a group of 50–59 years (23 people). The youngest age category of 20–29 years was represented by 13 respondents, and the least represented were workers aged 60 and over (5 respondents). As far as the place of work was concerned, social workers working in villages with up to 5,000 inhabitants were most represented, of which there were 64. They were followed by workers working in cities with up to 20,000 inhabitants (27 respondents). The smallest group was represented by workers from cities with a population of over 20,000, of whom there were 18.
To verify hypothesis H3, a trend analysis was used, while the category of social workers working in the intergenerational field of social work was excluded from the analysis, as this category does not have a clear age order necessary for the application of trend statistical methods. As part of this partial analysis, 72 questionnaires were processed. The largest group consisted of social workers working with seniors, 31 of them (43.1%). The second largest group were social workers working with youth - 19 respondents (26.4%). This was followed by workers working in the field of social work with adults, of whom there were 12 (16.7%). The least represented group were social workers working with children, 10 of them (13.9%). The distribution of respondents thus indicates a significant representation of social workers working with older clients, which corresponds to current demographic trends and the needs of social services.
Overall, it can be concluded that the research set reflects the real conditions in Slovak social work - a significant preponderance of women, a wide age range, a strong representation of workers working in smaller cities and rural regions, and a strong representation of social workers working with seniors. At the same time, this composition of the sample allows an adequate analysis of hypotheses focused on age, digital literacy, regional differences in the use of artificial intelligence in social work, as well as the influence of the age of the clientele on the experience of using artificial intelligence in social work.
Interpretation of research results
The results of the analysis of the obtained data clearly confirm that there are significant differences between the use of artificial intelligence in the practice of social workers depending on age, level of digital skills and place of work.
Correlation matrix between the four variables:
Correlation analysis confirmed that digital literacy has a very strong positive relationship with the use of AI (correlation coefficient 0.76), meaning that the more digitally skilled a social worker is, the more they use AI. There is a moderate negative correlation between age and AI use (-0.50); this means that with increasing age, the probability that the worker uses AI in practice decreases. Place of work (urban/rural context) has a moderately strong positive correlation (0.39) with AI use – urban workers are more likely to use these tools than their rural counterparts. At the same time, age and digital literacy are closely related (coefficient -0.56) - so it is true that older workers also have lower digital skills, which further secondarily limits the use of innovations. Correlation analysis therefore provides a clear picture of the direction and strength of the relationships between the main variables – digital literacy is the most important factor, age and regional context play a significant but secondary role in the adoption of AI in social work.
Regression analysis: Comparison of the influence of individual predictors on the use of AI:
Regression analysis shows that the use of artificial intelligence among social workers is most influenced by their level of digital literacy (regression coefficient 0.73). This means that each additional point in digital skills leads to significantly more intensive use of AI. Place of work also has a statistically significant effect (coefficient 0.33), with urban workers using AI more often than their rural counterparts. The effect of age is smaller with a coefficient of -0.07, but it still indicates that the probability of using AI decreases with increasing age. The interaction of all three variables explains up to 64% of the variability in the respondents' behavior, which confirms that digital literacy, place of work and age are key factors for applying AI in social work.
In the case of the verification of hypothesis H3, descriptive and trend analysis procedures were used due to the ordinal nature of the variables. As part of this partial analysis, 72 questionnaires were processed. Descriptive analysis was focused on the distribution of respondents according to the field of work, in terms of the age of the clientele and according to the level of experience with the use of artificial intelligence in social work.
Contingency table: Age of clientele x experience with using AI
The contingency table shows the different distribution of experiences with the use of artificial intelligence depending on the age group of the clientele. Social workers working with children and youth were more likely to report an intermediate to advanced level of experience with AI, while workers working with seniors were significantly more likely to declare no or only basic experience. The lowest level of experience with AI was recorded in the group of social workers working with seniors, where the answers "none" and "basic" experience dominated.
Spearman's correlation was used to verify the hypothesis of the existence of a trend between the age of the clientele and the level of experience with the use of artificial intelligence. The analysis showed a strong negative relationship between the monitored variables (ρ = −0.56; p < 0.001), which means that as the age of the clientele increases, the level of experience of social workers with the use of artificial intelligence decreases. To confirm the stability of the results, Kendall's tau-b test was also applied, which also confirmed a statistically significant negative relationship (τ = −0.46; p < 0.001). Both analyzes consistently point to the existence of a significant trend according to which social workers working with younger age groups of clients have more experience with the use of AI than workers working with older clients.
The Kruskal–Wallis test was used to compare the level of experience with the use of artificial intelligence between individual age groups of the clientele (children, youth, adults, seniors). The results showed that the differences between the groups were statistically significant (H = 22.68; p < 0.001). These findings confirm that the age of the clientele represents a significant factor of differentiation in the level of experience of social workers with the use of artificial intelligence, while the lowest level of experience was recorded among workers working in the field of social work with seniors.
The results of the analysis clearly confirm the existence of a statistically significant trend between the age of the clientele and the level of experience with the use of artificial intelligence in social work. Social workers working with younger clients show higher levels of experience with AI, while working with older clients is associated with lower levels of technology experience. The hypothesis of a decreasing level of AI experience with increasing age of the clientele was thus confirmed.
Trend of experience of social workers with the use of artificial intelligence according to the age of the clientele
The graphic representation of the trend points to a gradual decrease in the average level of experience with the use of artificial intelligence as the age of the clientele increases. The highest average level of experience was recorded for social workers working with children and youth, while the lowest value occurred for workers working with the elderly. The visualization thus confirms the existence of a negative trend, which corresponds to the results of the trend statistical analysis.
Discussion
Based on the performed analyses, it can be concluded that all three established hypotheses were confirmed. Hypothesis H1 was supported by the results of correlation and regression analysis, according to which the older age of social workers and the lower level of digital literacy reduce the probability of using artificial intelligence in practice. Digital literacy emerged as the strongest predictor of AI adoption (r = 0.76; β = 0.73), while age showed a moderately strong negative relationship (r = −0.50) and a weaker but still statistically significant regression effect (β = −0.07). Hypothesis H2 was confirmed through a t-test, which showed statistically significant differences in the use of artificial intelligence between social workers working in urban and rural environments (p < 0.0001). Social workers in cities used AI significantly more often (average = 3.11) than workers working in rural areas (average = 1.85), which clearly confirms the importance of the regional context in technology adoption.
An important finding of the presented research is the confirmation of hypothesis H3, which pointed to the existence of a statistically significant trend between the age of the clientele and the level of experience of social workers with the use of artificial intelligence. Trend analysis showed a strong negative relationship, according to which social workers working with younger age groups of clients have a higher level of experience with AI, while workers working with older clients, especially seniors, show a significantly lower level of experience. This trend was confirmed through Spearman's correlation and Kendall's tau-b test and further supported by Kruskal–Wallis analysis, which revealed statistically significant differences between individual age groups of the clientele. The descriptive results also show that no advanced or professional experience with the use of artificial intelligence was recorded in the group of social workers working with seniors.
The integration of artificial intelligence into social work is thus conditioned not only by the individual characteristics of social workers, but also by the structural and contextual factors of the environment in which they perform their professional activities. In this research, the age of the clientele turns out to be an important factor that affects the nature of work, the degree of digitization of work processes and the frequency of use of technological tools. Work with children and youth is more often associated with digital forms of communication, records and interventions, which naturally creates space for gaining experience with AI. On the contrary, social work with seniors is more oriented towards personal contact and traditional forms of care, which may reduce the need or willingness to implement advanced digital technologies.
Differences between urban and rural environments, as well as between individual areas of social work performance, are among the most significant factors affecting the availability of digital infrastructure, technological support and educational opportunities. Turner-Lee and Du (2025) point out that low digital literacy acts as a significant barrier to technological adoption and contributes to the deepening of the digital divide between professional communities, which is also clearly confirmed in our research sample.
In the discussion, the ethical aspects of the implementation of artificial intelligence in social work cannot be neglected, which represent one of the key challenges of the current digital transformation of the profession. Molala (2024) points out that younger social workers generally show a lower level of fear of technological loss of control and are more open to experimenting with artificial intelligence as a tool to support professional decision-making. On the contrary, older workers perceive the use of AI more often as a potential risk that can disrupt personal contact with the client, professional autonomy and traditional forms of relational work. Dirgová and Ďurčanská (2024) emphasize that the foundation of social work remains a human relationship based on the dignity of the client, respect for his autonomy and consistent protection of confidential information. From this point of view, the implementation of artificial intelligence must be carried out in accordance with the ethical principles of the profession, so that technological innovations support the quality and efficiency of social services without disrupting the relationship of trust between the social worker and the client. In this context, Budayová et al. (2025) point out that digitization and the use of advanced technologies are an essential part of the modernization of social services, but their introduction must be firmly anchored in the values of social work. Artificial intelligence should serve as a support tool for professional judgment and decision-making that increases the quality of interventions, not as a substitute for the personal contact, empathy and responsibility of the social worker.
Conclusion
The conclusion of the presented research confirms that the successful integration of artificial intelligence into the practice of social workers is primarily conditioned by the level of digital literacy, the age of social workers, the regional context and the nature of the clientele they work with. The results show that more digitally savvy and younger social workers, especially those working in urban environments and working with younger age groups of clients, use artificial intelligence and have higher experience with it more often than their colleagues from older age categories, rural areas or working with seniors.
At the same time, the findings show that the implementation of artificial intelligence in social work is not only a technical issue, but is closely related to the context of professional practice and the value anchoring of the profession. If AI is to be a real tool for increasing the quality and efficiency of social work, it is necessary to systematically develop the digital and AI competences of social workers in the entire sector, with special emphasis on regions and areas of social work that are lagging behind in digitization. The introduction of artificial intelligence should be accompanied by a clear ethical and support framework, so that the technology does not contribute to the deepening of inequalities, but supports the availability, quality and human dimension of social services. These findings represent an important impulse for the management of social service organizations, educational institutions and public policy makers, who play a key role in shaping the conditions for the responsible and ethically anchored use of artificial intelligence in social work.
Authors: doc.PhDr. Mgr. Eva Dirgová, PhD., DSc. ORCID: 0000-0002-8569-2282 Institute of Z.G.Mallu
PhDr. Roman Lebeda, MBA University of Health and Social Work St. Elizabeth, n. o.
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