Article: Narrative optimisation and audience immersive conversion mechanism of cultural tourism short drama driven by generative artificial intelligence Journal: International Journal of Arts and Technology (IJART) 2026 Vol.16 No.9 pp.182 - 200 Abstract: To address the challenges of severe content homogenisation and inefficient audience conversion pathways in cultural tourism short dramas, this study constructs a narrative optimisation framework driven by generative artificial intelligence (GAI). The framework leverages the large-scale, high-quality multilingual dataset for video-and-language research to extract rich audio-visual features. Multimodal large language models are employed to uncover the underlying cultural semantics and dramatic tension embedded in tourism landscapes. From a technical perspective, a cross-modal retrieval mechanism is introduced to achieve precise semantic alignment between landscape imagery and narrative text. Reinforcement learning from human feedback is further applied to dynamically refine the emotional dimensions of the generated narratives. Structural equation modelling is utilised to examine the underlying psychological mechanisms through which audience spatial presence evolves into travel intention. Inderscience Publishers - linking academia, business and industry through research

Title: Narrative optimisation and audience immersive conversion mechanism of cultural tourism short drama driven by generative artificial intelligence

Authors: Ruomu Miao; Shitao Li

Addresses: School of Art, Design and Media, East China University of Science and Technology, Shanghai, 200237, China ' Academic Affairs Office, Shanghai Theatre Academy, Shanghai, 200040, China

Abstract: To address the challenges of severe content homogenisation and inefficient audience conversion pathways in cultural tourism short dramas, this study constructs a narrative optimisation framework driven by generative artificial intelligence (GAI). The framework leverages the large-scale, high-quality multilingual dataset for video-and-language research to extract rich audio-visual features. Multimodal large language models are employed to uncover the underlying cultural semantics and dramatic tension embedded in tourism landscapes. From a technical perspective, a cross-modal retrieval mechanism is introduced to achieve precise semantic alignment between landscape imagery and narrative text. Reinforcement learning from human feedback is further applied to dynamically refine the emotional dimensions of the generated narratives. Structural equation modelling is utilised to examine the underlying psychological mechanisms through which audience spatial presence evolves into travel intention.

Keywords: generative artificial intelligence; GAI; cultural tourism short drama; narrative optimisation; multimodal large language models; immersive experience; conversion mechanism.

DOI: 10.1504/IJART.2026.155625

International Journal of Arts and Technology, 2026 Vol.16 No.9, pp.182 - 200

Received: 03 Apr 2026
Accepted: 14 May 2026
Published online: 07 Aug 2026 *

Title: Narrative optimisation and audience immersive conversion mechanism of cultural tourism short drama driven by generative artificial intelligence

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