Is Everyone an Artist? A Study on User Experience of AI-Based Painting System

被引:20
|
作者
Xu, Junping [1 ]
Zhang, Xiaolin [2 ]
Li, Hui [3 ]
Yoo, Chaemoon [1 ]
Pan, Younghwan [1 ]
机构
[1] Kookmin Univ, Dept Smart Experience Design, Seoul 02707, South Korea
[2] Guangdong Univ Technol, Coll Art & Design, Guangzhou 510006, Peoples R China
[3] Guangxi Normal Univ, Coll Fine Arts, Guilin 541006, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 11期
关键词
AI-Based Painting Systems (AIBPS); Technology Acceptance Model (TAM); behavioral intentions; user experience; Structural Equation Modeling (SEM); TECHNOLOGY ACCEPTANCE MODEL; INFORMATION-TECHNOLOGY; PERCEIVED USEFULNESS; FIT INDEXES; ADOPTION; TRUST; TAM; METAANALYSIS; INTENTIONS; MOTIVATION;
D O I
10.3390/app13116496
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Artificial Intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with an AI-based Painting System (AIBPS). A questionnaire was administered to 528 Chinese participants, and validated factor analysis data and Structural Equation Modeling (SEM) were used to test our hypotheses. The findings showed that Hedonic Motivation (HM) and Perceived Trust (PE) had a positive effect (+) on users' Perceived Usefulness (PU) and Perceived Ease of Use (PEOU), while Previous Experience (PE) and Technical Features (TF) had no effect (-) on users' Perceived Usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for the further extension of TAM, thus helping to improve the user experience of AIBPS. The results of this study provide insights for system developers and enterprises to better motivate users to use AIBPS.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Edge AI-based Forklift Safety Support System
    Takubo, Shinya
    Nagata, Atsuki
    Ikai, Shungo
    Li, Mofei
    Nishioka, Shingo
    Yoshimura, Akinobu
    SEI Technical Review, 2024, (98): : 71 - 76
  • [42] AI-based detection for Remote Electrocardiogram Monitoring System
    Garcia, Max
    Kumar, Sanjeev
    2024 IEEE 5TH ANNUAL WORLD AI IOT CONGRESS, AIIOT 2024, 2024, : 0336 - 0341
  • [43] AI-Based Facial Emotion Recognition Solutions for Education: A Study of Teacher-User and Other Categories
    Ravenor, R. Yamamoto
    ADVANCES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING, 2024, 4 (02): : 2128 - 2151
  • [44] AN AI-BASED CLASSIFICATION AND RECOMMENDATION SYSTEM FOR DIGITAL LIBRARIES
    Alomran A.I.
    Basha I.
    Scalable Computing, 2024, 25 (04): : 3181 - 3199
  • [45] Fully automatic AI-based leak detection system
    Tylman, Wojciech
    Kolczynski, Jakub
    Anders, George J.
    ENERGY, 2010, 35 (09) : 3838 - 3848
  • [46] An AI-Based Feedback Visualisation System for Speech Training
    Wynn, Adam T.
    Wang, Jingyun
    Umezawa, Kaoru
    Cristea, Alexandra, I
    ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II, 2022, 13356 : 510 - 514
  • [47] AI-based fruit identification and quality detection system
    Kashish Goyal
    Parteek Kumar
    Karun Verma
    Multimedia Tools and Applications, 2023, 82 : 24573 - 24604
  • [48] Towards a model-driven approach for multiexperience AI-based user interfaces
    Elena Planas
    Gwendal Daniel
    Marco Brambilla
    Jordi Cabot
    Software and Systems Modeling, 2021, 20 : 997 - 1009
  • [49] AI-based user authentication reinforcement by continuous extraction of behavioral interaction features
    Garabato, Daniel
    Dafonte, Carlos
    Santoveña, Raúl
    Silvelo, Arturo
    Nóvoa, Francisco J.
    Manteiga, Minia
    Neural Computing and Applications, 2022, 34 (14) : 11691 - 11705
  • [50] AI-based user authentication reinforcement by continuous extraction of behavioral interaction features
    Garabato, Daniel
    Dafonte, Carlos
    Santovena, Raul
    Silvelo, Arturo
    Novoa, Francisco J.
    Manteiga, Minia
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (14): : 11691 - 11705