Generative Artificial Intelligence Use Among Social Work Students: The Role of Perceived Utility and Knowledge

Abstract

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.

Keywords: Artificial intelligence; social work education; social work practice; technology adoption.