Session 3

AI for Social Wrok Education and Professional Development

P22  Constructing Theoretical Framework for Integrating VR Technology into Practical Teaching of Health Social Work: A Classic Grounded Theory Study

*Huamin Peng¹

¹Nanjing University, Jiangsu, China

Abstract

Background and Purpose: The application and development of virtual reality technology in social work practice teaching faces many obstacles, among which the most important reason is the lack of a mature teaching theoretical framework.

Methods: Therefore, this paper adopts the method of online written interviews, collects the prospective opinions and suggestions of 12 social work professional teachers, 5 postdoctoral and 10 social work professional students, and uses grounded theory to analyze the interview data.

Results: This paper explores the application of virtual reality technology in health social work practice teaching, analyzes its internal advantages and disadvantages and external realistic challenges. Finally, based on the research results and the micro theory of virtual reality technology integrated into practice teaching, this paper proposes a comprehensive practice teaching theoretical model, including four core components: “customized learning theory basic framework + purpose-oriented and emotional participation teaching case design + real and virtual scene integration teaching implementation + ethical plan and corresponding planning”.

Conclusions and Implications: This paper believes that this model can effectively use the advantages of virtual reality technology, while avoiding or reducing its risks and external obstacles, and provide a novel idea and method for (health) social work practice teaching.

P23  Integrating Artificial Intelligence into Social Work Education: Ethical Reflections and Practical Implications from a Student Perspective

*Bo Fu¹, Xinghua Ren¹, Jie Song¹, Jiyixi Hua¹, Tianming Bai¹, Yaling Shang¹, Zheng Xu¹

¹School of Philosophy and Sociology, Lanzhou University, Lanzhou, China

Abstract

Background and Purpose: Artificial Intelligence (AI) is increasingly being applied in social work education, for example through virtual client simulations and chatbot-based case interviews. However, empirical evidence on how students perceive and respond to these technologies remains fragmented. This study aims to synthesize existing research on social work students' use of AI in education, identify key themes regarding their attitudes and concerns, and derive practical implications for curriculum design and teaching practice.

Methods: An integrative literature review was conducted. Relevant studies were identified through database searches in Web of Science and Google Scholar using keywords such as "artificial intelligence," "social work education," "student attitudes,"and"ethical concerns." A total of 157 records were initially identified from Web of Science, and additional records were identified from Google Scholar. After duplicate removal and preliminary screening by titles and abstracts, approximately 45 relevant empirical studies focusing on social work students' use or perception of AI tools in educational contexts were included. Thematic synthesis was used to analyze the findings and identify recurring patterns and themes.

Results: Three main themes emerged from the synthesized literature. First, student attitudes: Most students hold moderately positive attitudes toward AI as a supplementary learning tool, but express strong reservations about AI replacing instructor-student interaction or weakening relational learning. Second, ethical concerns: The most frequently reported concerns include data privacy, algorithmic bias, and the risk that over-reliance on AI may weaken critical thinking and empathy. Third, factors influencing acceptance: Prior experience with AI tools (e.g., ChatGPT) is consistently associated with more positive attitudes.

Conclusions and Implications: Social work students are cautiously open to AI in education, but ethical concerns must be addressed proactively. Educators should position AI as an assistant rather than a replacement; provide hands-on training to build digital confidence; and integrate discussions of relevant ethics into AI-related assignments. Social work programs should develop clear guidelines on data protection, algorithmic transparency, and acceptable use of AI. Future research should conduct longitudinal studies to examine whether AI-assisted learning affects students' professional empathy and relationship-building skills in field practice.

P24  A Study on AI Dependency Among Young Adults Aged 21–24 and Social Work Interventions

*Rui Cao¹, Hongyan Yin¹, Yahui Li¹, Xuemei Wang¹

¹Lan Zhou Universiy, Lanzhou, China

Abstract

Background and Purpose: Against the backdrop of widespread AI adoption, young adults aged 21–24—who are in the transitional phase between education and employment—are prone to developing emotional dependence on AI while simultaneously exhibiting a widespread tendency to “cut ties with their parents,” presenting new challenges for social work services. This qualitative study aims to clarify the intrinsic connection and underlying logic between these two phenomena, define the core role and intervention pathways of social work, and provide empirical evidence for targeted interventions.

Methods: Centered on semi-structured interviews, the study screened 20 young adults aged 21–24 who exhibited significant emotional dependence on AI and tendencies toward “disengagement from family” via a questionnaire. One-on-one interviews were conducted focusing on emotional needs and family-related dilemmas, followed by data coding and thematic analysis.

Results: Pressure from academic advancement and employment has intensified young people's demand for low-cost emotional support, while AI's stress-free nature and instant adaptability have made it the primary source of emotional dependence. Intergenerational conflicts within families, excessive expectations, and boundary violations are the key reasons for young people's “disengagement from family,” with both phenomena reflecting a shift in their emotional needs toward a “comfort-oriented” model.

Conclusions and Implications: These phenomena result from digital transformation, the restructuring of emotional needs, and the weakening of family functions. Social work should assume the roles of emotional guide, intergenerational mediator, and resource integrator. By employing emotional interventions, establishing communication platforms, and integrating AI with social work services, we can resolve the “disengagement from family” dilemma and optimize the emotional support system for young adults.

P25  Human-Centered Core in the Shadow of AI: Investigating AI Literacy, Self-Efficacy and Usage Patterns of Social Work Students

*Zishan Dong¹, *Yuanyuan Ge¹, Zhifeng He²

¹School of Sociology, Beijing Normal University, Beijing, China; ²Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, China

Abstract

To systematically investigate the current status and development pathways of AI competencies among social work students, this study, grounded in social cognitive theory, employed a cross-sectional questionnaire survey design targeting university social work students to analyze the intrinsic relationships among their AI literacy, self-efficacy, and patterns of AI tool usage. The results revealed that the AI literacy and self-efficacy of the surveyed students were generally at a moderate level; the two were significantly positively correlated and jointly predicted the frequency and depth of AI tool usage. Students’use of AI was largely concentrated on general tasks such as information retrieval and literature organization, while its application in core practice areas—such as professional assessment and case intervention—remained superficial.In particular, there are significant shortcomings in their ability to identify ethical risks associated with AI and to critically integrate AI into practice. The study indicates that the transformation of AI literacy into professional competence is not a linear process but faces structural challenges related to ethical discernment and practical integration. In response to these challenges, AI literacy education should be systematically integrated into the social work curriculum to strengthen training in ethical reasoning and critical application, and to develop AI operation guidelines tailored to professional contexts.At the policy level, it is recommended that AI literacy be incorporated into the core competency framework for social workers. This study provides empirical evidence for talent development in AI-enabled social work, and future research could include longitudinal tracking and cross-sample comparative studies.

P26  "When AI Becomes the Answer": Standardization Risk and Ethical Reasoning in Social Work Teaching Simulations

*Yuran Shao¹

¹School of Social and Public Administration, East China University of Science and Technology, China

Abstract

Background and Purpose: Amid the digital transformation of social work, the development of artificial intelligence (AI) in social work teaching simulations in China has been rapid. It is creating new possibilities for more evidence-informed and professionalized training. Existing research shows that generative AI has advantages in generating standardized solutions, but it also has limitations of detachment from context and implicit technological bias. Compared with practice settings, teaching simulations are characterized by standardized training, leading students tend to view AI outputs as standard solutions, which places professional judgment at risk. To understand and mitigate the risk, this paper conducts a study on students' ethical reasoning and value negotiation with AI outputs in social work teaching simulations and proposes a value-awareness-centered guidance framework. The study aims to explore (1) how students understand and use AI in social work teaching simulations, (2) how they position the role of AI outputs in professional practice interactions, and (3) how they address client-related issues concerning professional relationships, values, and ethics in professional judgment.

Methods: This study adopts a qualitative design. Stimulated recall interviews were conducted with 12 social work students, using case texts and scenario materials provided by the researcher as stimuli. The interviews focused on respondents' processes of understanding, using, modifying, and reflecting on AI outputs, with particular attention to their value-based and ethical considerations. Each interview typically lasted 40 minutes. Interview data were transcribed and analyzed thematically using NVivo 15. All participant information was anonymized.

Results: The study found that respondents tended to use the problem analysis in AI outputs as standardized answers, showing reduced sensitivity to professional values and prioritizing the rationality and operability of intervention methods in the case, while paying limited attention to the influence of technological bias. Only when a case presented a clear ethical conflict, respondents tend to make value judgments and ethical analyses of the problem analysis and intervention suggestions in AI outputs. Furthermore, time pressure and reliance on the perceived "scientific" authority of AI may constitute important contextual conditions under which students pay less attention to technological bias and value negotiation.

Conclusions and Implications: This study argues that the central concern in AI-assisted teaching simulations for social work education is not whether AI-generated analyses are standardized or professionally aligned, but whether students are capable of applying value judgment and ethical reflection to AI outputs, and of transforming such outputs into interactive materials that can be jointly verified, interpreted, and negotiated with clients. This points toward the formation of a value-awareness-centered triadic professional relationship among the social worker, AI, and client.

P27  Identity Construction of AI Empathy Simulation in Social Work

*Yuechan Wang¹

¹School of Philosophy and Sociology, Lanzhou University, Lanzhou, China

Abstract

Background and Purpose: With the widespread application of artificial intelligence (AI) technologies in social work, AI empathy simulation systems (such as affective computing chatbots and virtual social work assistants) have gradually become important tools for service delivery. However, whether AI-simulated empathy influences the construction of social workers' professional identity remains underexplored. This study aims to examine: (a) how social workers perceive AI empathy simulation technologies; (b) how AI intervention reshapes social workers' professional self-identity; and (c) how the blurring boundaries of "empathy" attribution in human-AI collaboration affect professional identity. The hypothesis posits that the introduction of AI empathy simulation will generate "identity anxiety" among social workers and trigger the reconstruction of professional boundaries.

Methods: This study employed a mixed-methods design. In the quantitative phase, stratified random sampling was used to select practitioners from social work agencies across six cities in East China (N=312). The self-developed AI Empathy Perception Scale and Professional Identity Scale were administered, and Structural Equation Modeling (SEM) was employed to test variable relationships. In the qualitative phase, purposive sampling was used to recruit 18 social workers with extensive AI tool experience for semi-structured interviews, with data analyzed using grounded theory. Data collection was completed between March and August 2024.

Results: Quantitative findings revealed a significant negative correlation between frequency of AI empathy simulation use and professional identity (β=-0.34, p<0.001), with "technological replacement anxiety" serving as a mediator. Cluster analysis identified three identity construction patterns: "Technology Synergy Type" (32%), "Boundary Defense Type" (41%), and "Identity Loss Type" (27%). Qualitative analysis indicated that participants commonly experienced "empathy attribution dilemmas"—when AI demonstrated empathy-like responses, social workers developed cognitive conflicts regarding "what constitutes genuine professional empathy." Some practitioners reconstructed professional boundaries through "emotional labor redefinition" strategies.

Conclusions and Implications: The findings demonstrate that AI empathy simulation is reshaping core dimensions of social work professional identity. Recommendations include: (a) at the practice level, agencies should establish "human-AI collaboration ethical guidelines" to clarify AI assistance boundaries; (b) at the policy level, "professional identity in the digital era" should be incorporated as a mandatory module in continuing social work education; and (c) future research should track the long-term impact of AI technological iterations on professional ethics and explore identity construction differences across cultural contexts.

P28  Reconfiguring Professional Empathy: Social Workers’ Experiences of AI in Technology-Mediated Practice

*Ruiyang Sun¹, Xiaofang Jin¹

¹School of Philosophy and Sociology, Lanzhou University, Lanzhou, China

Abstract

The increasing integration of artificial intelligence is reshaping the practice of empathy in social work, and this study aims to explore how frontline practitioners experientially reconstruct and adapt professional empathy within technology-mediated contexts. Drawing on King’s (2011) three-dimensional framework of empathy, the study employs Interpretative Phenomenological Analysis (IPA) and conducts semi-structured, in-depth interviews with 10 to 15 frontline social workers who have experience using AI tools. Findings reveal that practitioners employ strategies of cognitive compartmentalization, affective energy distribution, and behavioral script integration, positioning AI primarily as an efficiency aid while reinforcing embodied presence during critical intervention moments—a process through which empathy becomes recontextualized. The study concludes that professional empathy in the digital age is not a static ethical attribute but a form of practical wisdom continuously negotiated within human–technology interactions, thereby offering a reflective knowledge base for advancing social work ethics education and digitally informed practice.

P29  Relationship Presence: A Study on the Mechanism of Digital Empathy Construction in Online Social Work

*Han Zhu¹, Ying Wang¹

¹School of Philosophy and Social Sciences, Lanzhou University, Lanzhou, China

Abstract

Background and Purpose: Digital transformation reshapes the interactive scene of social work tradition. How to build trust when the body is not present has become a problem both in theory and practice. This study focuses on the issue of how digital technology can intervene and reshape the core value of social work.

Methods: Based on the analytical framework of "system trust relationship space interpersonal trust", this study conducted multiple cross validation and theoretical sampling on in-depth interviews with 15 online social workers, 132 service interactive texts and 5 supervision records.

Results: The study found that online social workers realized the isomorphic reconstruction of "relationship presence" through the triple practical logic: first, the symbolic construction of the common situation. Social workers use "us" to express emoji, informal symbols such as phonetic and colloquial expressions to transform the "personal problems of service objects" into "social situations shared by all", which not only dispels the sense of distance brought by the system, but also helps service objects remove their sense of shame; second is the ethical consultation of interactive rhythm. Social workers are measured in response time and communication frequency, and find a balance between the expectation of the service object that they want to receive an immediate response and the protection of their own physical and mental health, as well as between professional norms and emotional temperature, so as to maintain the resilience of the relationship; third is the transformation and reciprocity of trust types.

Conclusions and Implications: The study revised the "substitution hypothesis" of Luhmann's system trust and interpersonal trust, and found that in China's local context, the two showed the cyclic enhancement characteristics of "system trust absorbs interpersonal trust and interpersonal trust feeds back system trust". Digital empathy is not a degraded copy of offline empathy, but a relationship oriented "relationship presence" based on technology mediation.

P30  The Dual Impact of Artificial Intelligence on Youth Social Work Practice — A Qualitative Study Based on Frontline Practitioners

*Yiting Wang¹

¹School of Sociology, Beijing Normal University, Beijing, China

Abstract

Background and Purpose: With Artificial Intelligence (AI) increasingly integrated into social services, its specific impact on youth social work—particularly from the frontline practitioner perspective—remains underexamined. This study systematically investigates the dual impact of AI applications in Chinese youth social work, exploring how AI both empowers and challenges professional practice, and examining the coping strategies employed by social workers.

Methods: A qualitative research design was employed, conducting semi-structured in-depth interviews with 15 frontline youth social workers selected through purposive and snowball sampling. Data collection focused on participants' firsthand experiences with AI tools in assessment, intervention, and administrative tasks. Interview transcripts were analyzed using thematic analysis to identify patterns related to perceived benefits, risks, and practical dilemmas of AI integration.

Results: The findings reveal AI's dual impact: positively enhancing efficiency through task automation, improving service targeting via data-driven assessment, and enabling innovative interventions; negatively presenting technical flaws (algorithmic bias, reliability, privacy risks), practical dilemmas (weakened professional judgment, adaptation barriers), and ethical tensions (devalued human connection, digital divide exacerbation, and limitations in processing tacit knowledge).

Conclusions and Implications: The study concludes that AI serves as both a valuable tool and source of professional anxiety in youth social work. It highlights the necessity of developing human-AI collaborative models that reinforce practitioners' critical agency, establish ethical guidelines and risk governance frameworks, and balance technological efficiency with social work's humanistic values. These findings provide empirical evidence to inform responsible digital transformation in Chinese social work practice.

P31  Navigating Challenges and Triumphs: The Lived Experiences of Social Workers in RSCC-Region III

Ms. Julyanna Bea Cornejo¹, Jerylle Louise Justo¹, Glaizza Maye Macawile¹, Ma. Elena Garcia¹, *John Carlo Perez¹, Milagros Reusora¹

¹Centro Escolar University, Manila, Philippines

Abstract

Background and Purpose: social workers play a vital role in assisting individuals and groups in navigating life challenges that may disrupt social functioning; however, their own lived experiences within demanding work environments often remain underexplored.

Methods: This phenomenological study focused on the lived experiences of 7 social workers employed at the Reception and Study Center for Children (RSCC) in Region III in Lubao, Pampanga. Anchored in Bronfenbrenner’s Ecological Systems Theory, the study was designed to understand how professional experiences influence social workers’ reflective practice, personal well-being, and coping strategies in addressing emotional and psychological challenges.Using a phenomenological research design, data were collected through face-to-face interviews guided by a researcher-made questionnaire to allow systematic yet open and unrestricted expression of participants’ experiences. Data were analyzed using thematic analysis to capture the essence and meaning of their shared experiences.

Results: Findings revealed that social workers often experience underappreciation, inadequate resources, outdated facilities, and financial constraints due to limited institutional funding, contributing to emotional and physical exhaustion. Despite these challenges, participants demonstrated resilience, commitment, and effective coping strategies rooted in passion for their profession. The ability to positively impact the lives of children served as a primary source of fulfillment and motivation.

Conclusions and Implications: This study provides insights into the often unseen realities of social work practice and emphasizes the need for stronger institutional support to promote social workers’ well-being and sustainability in practice.