Journal of evidence-based social work (2019)最新文献

Purpose: This theoretical analysis aims to develop a comprehensive rights-based framework for navigating artificial intelligence integration in social work practice while addressing the ethical implications of AI deployment across micro, meso, and macro practice levels.

Materials and methods: The study synthesized interdisciplinary research drawing on social psychology, group dynamics theory, and institutional analysis. The conceptual framework integrated the I-C-E (Ingroup Identification, Cohesion, Entitativity) model with socioecological systems theory. Analysis was conducted on existing literature and documented case examples to examine how AI systems mediate interpersonal relationships and construct meaning in social work contexts.

Results: The analysis demonstrated that AI systems profoundly impact vulnerable populations by mediating interpersonal relationships and constructing meaning in AI-mediated environments. The developed framework successfully bridged social work theory with interdisciplinary insights to provide evidence-based guidance for AI implementation in social services.

Discussion: The proposed framework offers concrete strategies for social work education and provides research methodologies that center community voices. The analysis reveals how AI integration can be guided by evidence-based practice while maintaining focus on vulnerable population needs and democratic governance principles in social services.

Conclusion: This work provides evidence-based guidance for practitioners to harness AI's potential while safeguarding social work's core values of human dignity, self-determination, and social justice. The framework includes policy recommendations for democratic governance of AI in social services and establishes a foundation for ethical AI deployment across all levels of social work practice.

Purpose: This study investigates the inter-rater reliability between human experts (a forensic psychologist and a social worker) and a large language model (LLM) in the assessment of child sexual abuse statements. The research aims to explore the potential, limitations, and consistency of this class of AI as an evaluation tool within the framework of Criteria-Based Content Analysis (CBCA), a widely used method for assessing statement credibility.

Materials and methods: Sixty-five anonymized transcripts of forensic interviews with child sexual abuse victims (N = 65) were independently evaluated by three raters: a forensic psychologist, a social worker, and a large language model (ChatGPT, GPT-4o Plus). Each statement was coded using the 19-item CBCA framework. Inter-rater reliability was analyzed using Intraclass Correlation Coefficient (ICC), Cohen's Kappa (κ), and other agreement statistics to compare the judgments between the human-human pairing and the human-AI pairings.

Results: A high degree of inter-rater reliability was found between the two human experts, with the majority of criteria showing "good" to "excellent" agreement (15 of 19 criteria with ICC > .75). In stark contrast, a dramatic and significant decrease in reliability was observed when the AI model's evaluations were compared with those of the human experts. The AI demonstrated systematic disagreement on criteria requiring nuanced, contextual judgment, with reliability coefficients frequently falling into "poor" or negative ranges (e.g. ICC = -.106 for "Logical structure"), indicating its evaluation logic fundamentally differs from expert reasoning.

Discussion: The findings reveal a profound gap between the nuanced, contextual reasoning of human experts and the pattern-recognition capabilities of the LLM tested. The study concludes that this type of AI, in its current, prompt-engineered form, cannot reliably replicate expert judgment in the complex task of credibility assessment. While not a viable autonomous evaluator, it may hold potential as a "cognitive assistant" to support expert workflows. The assessment of child testimony credibility remains a task that deeply requires professional judgment and appears far beyond the current capabilities of such generative AI models.

Purpose: This research explores clinical social workers' perceptions of the usefulness of generative artificial intelligence (AI) in clinical practice, with a particular focus on large language models (LLMs).

Materials and methods: This qualitative reflexive thematic analysis explored the interviews of 21 clinical social workers and how they experience their work in the context of growing LLM use. Participants shared their perceptions and experiences with LLMs following a collaborative case consultation exercise using ChatGPT and a video demonstration of a client using ChatGPT.

Results: Social work practitioners described both benefits and concerns with LLM use in their practice. Two overarching themes emerged: (1) factors that enhanced social workers' perceived usefulness of LLMs in clinical practice, including support for administrative tasks and client engagement, and (2) factors that diminished perceived usefulness, such as concerns about confidentiality, loss of nuance, and limitations in conveying empathy and contextual understanding.

Discussion: Practitioners shared that they are using LLMs as idea generators in clinical work, while simultaneously expressing concern about the quality of information and the need for a human‑centered approach. They also noted that their decision to adopt LLMs is shaped by professional ethics and relational values, reflecting a preference for augmentation rather than full automation to preserve therapeutic depth and client wellbeing.

Conclusion: Future AI implementation should focus on practitioner training and clear ethical guidelines to support responsible integration of LLMs. Ongoing evaluation will be essential to ensure these tools enhance clinical practice without compromising the therapeutic relationship or core social work values.

The integration of artificial intelligence (AI) into mental health care presents both profound opportunities and pressing ethical responsibilities for the social work profession. As social workers strive to deliver equitable, client-centered, and evidence-based care, AI offers tools to enhance diagnostic accuracy, streamline treatment planning, and increase access to current research. However, adopting AI also raises critical concerns, including algorithmic bias, data privacy, and the potential erosion of human-centered practice. This editorial argues that social workers have an ethical imperative to engage with AI technologies and proactively shape their development and application to align with the profession's values. By actively participating in interdisciplinary AI initiatives, advocating for transparency and inclusion, and ensuring that AI tools are used to support rather than supplant human judgment, social workers can help ensure that technological innovation serves the diverse needs of clients and communities. The editorial concludes by outlining key areas for social work leadership, including research translation, equitable AI access, and ethical governance, emphasizing that the future of mental health care depends on ethically grounded, socially responsible innovation.

The purpose of this introductory article is three-fold: (1) to share with the reader the inspiration for the present special issue; (2) to describe for the reader the array of articles published in this special issue and the organizing logic; and (3) to offer for the readers' consideration a proposed conceptual framework for understanding the ethical role of Artificial Intelligence in the social work profession. In 2024, David Edmonds and a team of leading philosophers published AI Morality. Edmonds identified six emergent themes based on the authored chapters: autonomy; bias; responsibility; privacy and transparency; meaning; and, values and morals. We explore these six themes as a viable complement to the Belmont Report and the National Association of Social Workers (NASW) Code of Ethics for understanding categorical ethical concerns related to the use of AI in social work research, and in this way, evidence-based social work practice and broader society. We thematically grouped the corresponding relationships and used these groupings as an organizing framework: (1) autonomy, power/oppression, and informed consent; (2) bias, discrimination, and social justice; (3) responsibility, harm, and competence; (4) privacy, confidentiality, and transparency; (5) meaning, service, and social need; and, (6) values, morality, and ethical alignment. We identify ethical concerns across these six categories and make respective recommendations before offering final thoughts.

Purpose: This study explores how artificial intelligence (AI) can be integrated into social work practice by examining both its potential opportunities and associated challenges. The research aims to determine how AI technologies can support social workers in delivering more effective, accessible, and ethical services, and to identify the professional training needs that may arise from this digital transformation.

Method: Using an interpretative phenomenological approach grounded in human-centered and ethical social work principles, data were collected through semi-structured interviews with 23 social workers from diverse fields in Türkiye and analyzed thematically with MAXQDA.

Findings: Participants identified several advantages of AI integration, including enhanced risk analysis, rapid intervention capacity, improved service quality, cost-effectiveness, and easier access for disadvantaged populations. However, they also emphasized challenges such as the loss of human-centered approaches, ethical and privacy risks, insufficient technological infrastructure, and potential employment concerns.

Originality and contribution: The study contributes to the limited qualitative research on AI in social work by presenting practice-based insights from professionals. It emphasizes the need for comprehensive, ethics-oriented AI education and policy development to ensure technological innovation aligns with the profession's humanistic values. It also highlights the importance of addressing conceptual tensions between technological innovation and human-centered practice, offering insights to inform AI-focused training and education in social work.

Conclusions: While AI offers significant opportunities for innovation and inclusion, its integration must be guided by ethical standards, professional training, and adequate infrastructure to ensure that it complements rather than replaces the relational foundations of social work.

Existing research documents that in a technologically connected society, digital media can often shift population-level ideologies surrounding social justice and social work. Additionally, evidence indicates increased digital-media consumption patterns given how marginalized individuals can face greater barriers in physical participation. Based upon such rationale, this conceptual paper investigates how artificial intelligence serving as digital-media creation tools may impact the lived experience of those who face marginalization due to age, gender, race, Indigenous ancestry, rural geography, disability, and socioeconomic status. This paper reports qualitative data from a parent study engaging with marginalized individuals (n = 8) experiencing 1) intersectional statuses associated with stigma and 2) ongoing barriers to participation in formal learning opportunities, to assess how digital media play critical roles in shaping their access to new information, beliefs, and worldviews. Informed by anti-oppressive and trauma-informed principles in social work, the research employed semi-structured interviews guided by a collaboratively developed framework - CATER (Collection, Action, Transformation, Emotion, Recommendation). Participants reported an average of more than three statuses of marginalization, and were invited to share their lived experiences - specifically informing how marginalization impacts their autonomous creation of digital media and engagements with machine-learning technologies. A sevenpart framework of AI in social-work-oriented digital media creation is thus conceptualized to consider: inclusivity in narrative dissemination; financial barriers intersecting with socioeconomic status; dominant versus counternarratives; market influences; and AI's critical shortcomings in terms of visibility and audience receptivity. Implications for social justice and social work with marginalized groups conclude this study.

Purpose: Artificial intelligence (AI) presents unique advancements in technology that involve both challenges and opportunities. However, student perspectives regarding the multifaceted impact of AI are less known in the current literature. To address this gap, the current study was undertaken to explore social work students' perceptions and concerns associated with AI technologies.

Materials and methods: We conducted structured interviews with students (n = 15) in social work programs. We developed an interview guide with a list of questions to ask students, and no prior knowledge of AI was required by the students.

Results: The data were analyzed using a thematic analysis approach that resulted in five themes: 1) Increased efficiency, 2) Ethical considerations, 3) Risk concerns, 4) Psychological impacts, and 5) Societal impacts.

Discussion: The social work discipline needs to augment efforts into research on the utility of AI in social services delivery and social work education. There is also a need to explore students' perspectives on the use of AI technologies and the potential ways in which these technologies can be used by educators and social work professionals to increase efficiency in social services while mitigating identified risks, ethical concerns, and psychosocial impacts. Recommendations are made regarding digital literacy, enhanced student learning, ethics, and accreditation standards.

Conclusion: Our study highlights the need to gain an understanding of how AI technologies influence human perception and provides recommendations for better integration of AI in social work educational curricula and ways to promote AI among students, given its ethical implications and practical application.

Purpose: As generative Artificial Intelligence (AI) expands across academic and professional fields, its integration into human-centered professions like social work remains complex. Limited research explores how social workers engage with these technologies in the United States. This study examines how perceived utility and knowledge influence AI usage among social work students.

Materials and methods: A cross-sectional survey exploring attitudes toward AI, perceived utility, knowledge, and frequency of use was administered to students at a southeastern United States university. Principal Components Analysis assessed the factor structure of attitude items, and regression models determined associations with generative AI use.

Results: Principal Component Analysis identified clear dimensions of AI attitudes. Regression models indicated that both perceived utility and AI knowledge were significant predictors of use when controlling for other factors, suggesting emerging social workers engage with AI tools more frequently when they find them useful and feel knowledgeable about AI. Prior knowledge did not moderate the effect of perceived utility.

Discussion: These findings underscore the necessity to design trainings and curricula that highlight AI's practical utility while imparting knowledge on effective and ethical utilization. By fostering responsible engagement with emerging technologies, those training social workers can prepare future practitioners to navigate an evolving digital landscape while upholding core professional values.