In-Situ Mode: Generative AI-Driven Characters Transforming Art Engagement Through Anthropomorphic Narratives
<|reference_start|>In-Situ Mode: Generative AI-Driven Characters Transforming Art Engagement Through Anthropomorphic Narratives: Art appreciation serves as a crucial medium for emotional communication and sociocultural dialogue. In the digital era, fostering deep user engagement on online art appreciation platforms remains a challenge. Leveraging generative AI technologies, we present EyeSee, a system designed to engage users through anthropomorphic characters. We implemented and evaluated three modes (Narrator, Artist, and In-Situ) acting as a third-person narrator, a first-person creator, and first-person created objects, respectively, across two sessions: Narrative and Recommendation. We conducted a within-subject study with 24 participants. In the Narrative session, we found that the In-Situ and Artist modes had higher aesthetic appeal than the Narrator mode, although the Artist mode showed lower perceived usability. Additionally, from the Narrative to Recommendation session, we found that user-perceived relatability and believability within each interaction mode were sustained, but the user-perceived consistency and stereotypicality changed. Our findings suggest novel implications for applying anthropomorphic in-situ narratives to other educational settings.<|reference_end|>
@article{li2024in-situ,
    title={In-Situ Mode: Generative AI-Driven Characters Transforming Art
  Engagement Through Anthropomorphic Narratives},
    author={Yongming Li, Hangyue Zhang, Andrea Yaoyun Cui, Zisong Ma, Yunpeng
  Song, Zhongmin Cai, and Yun Huang},
    journal={arXiv preprint arXiv:2409.15769},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15769},
    primaryClass={cs.HC}
}