'I Recall the Past': Exploring How People Collaborate with Generative AI to Create Cultural Heritage Narratives

Visitors to cultural heritage sites often encounter official information, while local people's unofficial stories remain invisible. To explore expression of local narratives, we conducted a workshop with 20 participants utilizing Generative AI (GenAI) to support visual narratives, asking them to use Stable Diffusion to create images of familiar cultural heritage sites, as well as images of unfamiliar ones for comparison. The results revealed three narrative strategies and highlighted GenAI's strengths in illuminating, amplifying, and reinterpreting personal narratives. However, GenAI showed limitations in meeting detailed requirements, portraying cultural features, and avoiding bias, which were particularly pronounced with unfamiliar sites due to participants' lack of local knowledge. To address these challenges, we recommend providing detailed explanations, prompt engineering, and fine-tuning AI models to reduce uncertainties, using objective references to mitigate inaccuracies from participants' inability to recognize errors or misconceptions, and curating datasets to train AI models capable of accurately portraying cultural features.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-05-02
DOI
https://doi.org/10.1145/3711006
Citations
23
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
122.87
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article

'I Recall the Past': Exploring How People Collaborate with Generative AI to Create Cultural Heritage Narratives

Ray Lc, L. H. Chen, Tianqi Wang, Jiayi Su et al.
23 citations
Proceedings of the ACM on Human-Computer Interaction
Computational and Text Analysis Methods
122.87
article

'I Recall the Past': Exploring How People Collaborate with Generative AI to Create Cultural Heritage Narratives

Ray Lc, L. H. Chen, Tianqi Wang, Jiayi Su, Zhiting He
article en
23 citations

Abstract

Visitors to cultural heritage sites often encounter official information, while local people's unofficial stories remain invisible. To explore expression of local narratives, we conducted a workshop with 20 participants utilizing Generative AI (GenAI) to support visual narratives, asking them to use Stable Diffusion to create images of familiar cultural heritage sites, as well as images of unfamiliar ones for comparison. The results revealed three narrative strategies and highlighted GenAI's strengths in illuminating, amplifying, and reinterpreting personal narratives. However, GenAI showed limitations in meeting detailed requirements, portraying cultural features, and avoiding bias, which were particularly pronounced with unfamiliar sites due to participants' lack of local knowledge. To address these challenges, we recommend providing detailed explanations, prompt engineering, and fine-tuning AI models to reduce uncertainties, using objective references to mitigate inaccuracies from participants' inability to recognize errors or misconceptions, and curating datasets to train AI models capable of accurately portraying cultural features.

Proceedings of the ACM on Human-Computer InteractionVol. 9(2)
Guangdong University of Technology (CN), City University of Hong Kong (HK), Guangzhou Academy of Fine Arts (CN), Communication University of China (CN)
Sustainable cities and communities
Openalex Percentile: Top 0%
Computational and Text Analysis Methods
122.87
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