Artificial Intelligence and Case-Based Learning in Social Work Education: Pedagogical Considerations and Future Directions

As artificial intelligence (AI) becomes more prevalent in higher education, it raises important pedagogical, ethical, and disciplinary questions. This theoretical paper develops a comprehensive theoretical foundation for considering the use of AI-generated case studies in social work education and examines the implications for teaching, learning, and professional identity formation. The theoretical framework is not exhaustive. Nevertheless, by drawing on Entwistle’s conception of deep learning, Biggs and Tang’s constructive alignment, new materialist perspectives on classroom technology assemblages, and critical discursive frameworks informed by Foucault and Packer, this paper argues that AI-generated case studies can enhance social work students’ reflexivity when used with careful oversight. AI can expand the discursive space, diversify representational possibilities, and save lecturers’ preparation time. Nevertheless, AI presents significant risks related to bias, representational ethics, and over-standardisation. Informed by practice realities, risks can be mediated by drawing on practitioner wisdom to orchestrate AI case study prompts and anchor AI-produced narratives. This theoretical paper emphasises the need for critical AI literacy, transparent governance, and educator–practitioner collaboration to ensure that AI serves as a tool for deep, anti-oppressive, and justice-oriented learning. AI-assisted case study pedagogy is shown to be most valuable when it strengthens, rather than diminishes, human judgement, reflexive practices, and relational social work education. This is a theoretical and conceptual paper. It does not report any empirical findings. Instead, it offers a structured synthesis of relevant pedagogical, philosophical, and practice-based literature to develop a framework for critically integrating AI-generated case studies into social work education.