Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations - Zvinodashe Revesai; Benjamin Tungwa; Telson Anesu Chisosa; Vanessa Runyararo Meki (IJIE (Indonesian Journal of Informatics Education), 2024)
Social work education in developing countries faces significant challenges, including limited resources, restricted access to current knowledge, and inadequate training opportunities. This study aims to examine the potential of emerging Artificial Intelligence (AI) technologies in empowering social work students by enhancing access to information through machine translation and intelligent search tools, improving resource availability via virtual simulations and adaptive learning platforms, and integrating AI-powered self-help tools into the curriculum. A qualitative research design was employed, utilizing in-depth interviews with 16 educators and 8 field training officers, along with focus group discussions involving 24 social work students across selected institutions in Zimbabwe. All interviews were audio-recorded with participant consent, with translators assisting where necessary for local languages. Additional data were collected from documents, public reports, learning platforms, and policy papers to provide context on AI adoption strategies. Data were analyzed using thematic analysis, examining cases and models where AI has expanded access to scholarly materials through automated translation services, enabled localized resources through virtual training simulations, and facilitated the incorporation of culturally aligned self-help tools such as AI chatbots and wellness applications. The findings show that, with careful implementation and consideration of the context, artificial intelligence can reduce inequalities in education and enhance students' abilities through personalized learning paths, virtual environments for practice, and automated feedback systems. However, this research points out the need for addressing the digital divide and ethical issues associated with artificial intelligence, including problems of privacy and algorithmic bias. The study concludes by making a call for further research into models of safe and equitable AI integration in social work education.