AI-Assisted Social Work Education, Innovations, and Training Among Tertiary Institutions in Africa

This study explored the integration of Artificial Intelligence (AI) in social work education across tertiary institutions in Africa, aiming to assess indices such as innovations, training approaches, implementation challenges, and post-graduation impact. This study adopted a content analysis method, which systematically examined documents and digital materials used to identify patterns, themes, and insights on AI-assisted social work education. The study also revealed that AI has introduced significant innovations such as personalized learning, digital case studies, and interactive training simulations. Educators and personnel are being trained to use AI tools effectively, though challenges such as inadequate infrastructure, limited digital skills, and policygaps hinder progress. Strategic solutions recommended include targeted ICT investment, updated curricula, and robust policy support. In addition, AI enhances post-graduation practice by improving decision-making and service delivery in social work. The study concluded that AIassisted education brings transformative benefits to social work training through personalizedlearning, simulations, and improved data use, enhancing student engagement and professional readiness. Despite these advances, many institutions struggle with poor infrastructure, limited digital literacy, and underfunding. Training for educators remains a pressing need. The study also suggested among others that AI assisted social work education should be implemented at the University to improve case management simulations for student learning, innovations in social work education through AI should be developed at the University to enable personalized learning modules for students, training of social work educators and personnel should be conducted at University to equip them with practical skills in AI tools for social intervention and challenges in implementing AI in social work education should be addressed at the University by reviewing infrastructure gaps.