Teaching GenAI in Museum Curation: Pathways to Digital-Intelligent Transformation

Generative Artificial Intelligence (GenAI) is reshaping museum curation practices through its innovative applications. This study investigates GenAI's multifaceted roles in curation workflows by analyzing teaching outcomes from Zhejiang University's Exhibition Planning and Design course, where Large Language Model (LLM) have been integrated as collaborative tool. Empirical findings reveal that GenAI can act as a creative co-pilot for ideation, an interdisciplinary knowledge synthesizer, and a real-time evaluator for exhibition narratives. Strategic prompt engineering and human-machine collaboration critical dialogue have been identified as key drivers to enhance content diversity, workflow efficiency, and cross-domain innovation. However, limitations persist, including output homogenization, factual inaccuracies, data dependency, and limited physical context awareness, all demanding rigorous human oversight. The paper concludes with actionable frameworks for implementing GenAI in curation, balancing technological empowerment with curatorial agency.