CurateXelerator: A Collaborative Human-AI Framework for Accelerating Curatorial Practice

Qinyan Li; James She

doi:10.53941/tai.2025.100020

Abstract

Curatorial texts are essential interpretive tools in art exhibitions, bridging the communication between artworks, curators, and visitors. While advancements in AI, particularly large language models (LLMs), have opened new possibilities for automating and assisting in the creation of curatorial texts, current AI models often suffer from inaccuracies and limited interpretive depth. This paper proposes “CurateXelerator”, a collaborative Human-AI framework that integrates a structured input strategy into the curatorial workflow to address these challenges. Comprehensive evaluations demonstrate that proposed method generates texts significantly superior to baseline AI prompts in narrative coherence, rhetorical style, and adherence to constraints. Crucially, in a human paired-samples study, texts by CurateXelerator earned an average quality rating of 3.17/5, achieving statistical parity with texts from human writers, which averaged 2.89/5, while significantly outperforming them in technical simplicity and logical structure. This research contributes a validated collaborative Human-AI framework that enhances curatorial efficiency and quality, a reusable dataset and benchmark for future cutorial practice, and critical insights for integrating AI into Human-centered creative practices.

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