Promoting AI literacy in social work education: lessons from an interdisciplinary course for social work students

Abstract

Artificial intelligence (AI), particularly generative AI and large language models (LLMs), are reshaping the landscape of social work practice. Schools of social work are only beginning to respond to these changes. This article presents findings from an interdisciplinary summer school course designed to promote AI literacy among social work students. The curriculum combined a range of interactive learning experiences with time for ethical reflection, and a developmental experience of creating domain-specific CustomGPTs. In order to understand student’s acquisition of AI knowledge and skills, we used an AI literacy measure called the ‘Scale for the assessment of non-experts’ AI literacy’ (SNAIL). We assessed changes in AI literacy through pre- and post-course self-assessments based on the SNAIL questionnaire. The results show statistically significant and potentially meaningful gains in AI literacy. The responses suggest that the course helped increase AI knowledge, enabling students to shift from hesitant observers to confident users of and thinkers about AI tools. This study contributes to the emerging field of AI literacy in social work by offering a model for interdisciplinary teaching on generative AI content.