Cultivating museum professionals: Rethinking talent development through teaching practice

Abstract

Using action research methodology, the study analyzes an undergraduate course grounded in double-loop learning and scaffolding theory, in which generative artificial intelligence (generative AI) was introduced as a learning partner rather than a substitute for curatorial expertise. Centered on the theme of local food and cultural memory, students engaged in staged activities combining AI-supported exploration, collaborative writing, and scenario-based reflection. Findings suggest that this approach supported students’ understanding of curatorial logic, narrative coherence, and the situated nature of museum work, while fostering critical engagement with AI-generated content. The study offers a context-specific example while pointing to broader questions about whether museum education is keeping pace with the technological environments its graduates will enter.