Artificial intelligence competences for social workers: a systematic literature review and training framework for higher education

This article presents a systematic literature review (SLR) of the evidence base on artificial intelligence (AI) competences required in social work education, in response to the accelerated digital transformation of social services across diverse national contexts. Following the PRISMA 2020 guidelines, a total of 87 peer-reviewed studies, professional frameworks, and institutional reports published between 2015 and 2025 were systematically reviewed from five academic databases: Web of Science, Scopus, PubMed, EBSCO, and Google Scholar. The review was conducted within an international scope, drawing on evidence from Europe (including the United Kingdom, Spain, the Nordic countries, and Denmark), North America, and Australia, and applies to statutory, third-sector, and community social work settings as broadly defined in the professional literature. Four core dimensions of AI competence emerge systematically from the reviewed literature: (1) technical literacy, encompassing foundational understanding of AI and machine learning systems and data literacy; (2) critical and ethical thinking, including the detection of algorithmic bias—defined as systematic and unfair discriminatory effects produced by AI systems as a result of biased training data or erroneous assumptions—and the maintenance of professional judgment; (3) communication and collaboration within multidisciplinary and interdisciplinary teams; and (4) pedagogical integration, addressing how these competences can be embedded sustainably into higher education curricula. The evidence consistently demonstrates a significant gap between the accelerating adoption of AI in social services and the inadequate provision of AI training in professional education programs. On the basis of these systematic findings, this article proposes a structured AI competence framework for social work education, organized into three progressive levels and explicitly grounded in the core disciplinary values of social justice, human rights, and human dignity. The framework is designed to prepare professionals to engage responsibly and critically with AI systems while preserving the person-centered foundations of the profession.