Advanced Industrial SCIence (산업과 과학)

Convergence Society for SMB (중소기업융합학회)
권호 표지
DOI QR코드

DOI QR Code

Generative AI and Public Perception : Survey Findings on Image-generated AI

생성형 AI와 대중의 인식: 이미지 생성형 AI에 대한 설문조사 결과

  • Won-Young Jeon (Department of Computer Science, Kongju National University) ;
  • Jae-Woong Kim (School of Software, Kongju National University) ;
  • Eun-Young Oh (Department of Nursing, Catholic Kkottongnae University)
  • 전원영 (국립공주대학교 컴퓨터공학과 ) ;
  • 김재웅 (국립공주대학교 소프트웨어학과) ;
  • 오은영 (가톨릭꽃동네대학교 간호학과)
  • Received : 2025.05.07
  • Accepted : 2025.07.20
  • Published : 2025.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study was to investigate public perception, usage experience, and satisfaction with image-generated AI(e.g., Midjourney, DALL-E) and to identify the factors influencing on satisfaction. A total of 2,275 participants were surveyed, and the collected data were analyzed using t-test, ANOVA, and multiple regression analysis via SPSS WIN 22.0 program. The result showed that 52.9% of participants had a positive attitude, while 47.1% held a negative attitude. The main reason for positive perceptions was 'convenience', whereas negative perceptions were 'unclear copyright problem'. Factors that significantly influenced satisfaction with generative AI included age group (ß=.047, p=.021) and usage experience (ß=.133, p<.001). The results show that social acceptance is slow compared to the speed of technological development in Generative AI, suggesting the need to establish legal and ethical standards and develop personalized AI.

본 연구의 목적은 이미지 생성형 AI(예: 미드저니, 달리)에 대한 대중의 인식, 사용 경험 및 만족도를 파악하고 만족도에 미치는 영향을 규명하는 것이다. 연구 대상자는 총 2,275명이었으며, 수집된 자료는 SPSS WIN 22.0 프로그램을 이용하여 t-test, ANOVA, 다중회귀분석을 통해 분석하였다. 연구 결과, AI에 대한 태도는 52.9%가 긍정적, 47.1%는 부정적인 것으로 나타났다. 긍정적 인식의 주요 이유는 '편리함'이었으며, 부정적 인식은 '불분명한 저작권 문제'이었다. 생성형 AI 만족도에 유의미한 영향을 미치는 요인은 연령대(ß=.047, p=.021), 사용 경험(ß=.133, p<.001)이었다. 이 결과는 생성형 AI의 기술 발전 속도에 비해 사회적 수용이 더디다는 점을 보여주며, 법적·윤리적 기준 마련과 개인 맞춤형 AI 개발의 필요성을 시사한다.

Keywords

Acknowledgement

This work was supported by the research grant of Kongju National University in 2024

References

  1. Sengar, S. S., Hasan, A. B., Kumar, S., & Carroll, F. (2024). Generative artificial intelligence: A systematic review and applications. Multimedia Tools and Applications, 1-40. DOI : 10.1007/s11042-024-20016-1
  2. Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2018). Multimodal machine learning: A survey and taxonomy. IEEE transactions on pattern analysis and machine intelligence, 41(2), 423-443. https://doi.org/10.1109/TPAMI.2018.2798607
  3. Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(2), 123-134. https://doi.org/10.1093/pnasnexus/pgae052
  4. Kim, S. A. (2019). Digital sexual violence and male-centered sex culture. Gender Review, 53, 61-67.
  5. Kim, H. (2020). Current status of response to digital child sexual slavery and comparative analysis of overseas crime prediction system using artificial intelligence. Journal of Digital Convergence, 18(7), 357-368. DOI : 10.14400/JDC.2020.18.7.357
  6. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems. Theory and Results/Massachusetts Institute of Technology.
  7. Davis F. D., Bagozzi R. P., & Warshaw P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of Applied Social Psychology. 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
  8. Ajzen I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes. 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  9. Taylor S, & Todd P. A. (1995). Understanding information Technology Usage: A test of competing models. Information Systems Research. 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
  10. Thompson R. L., Higgins C. A, & Howell J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143. https://doi.org/10.2307/249443
  11. Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research, 432-448.
  12. Compeau D, Higgins C. A., & Huff S. (1993). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158. https://doi.org/10.2307/249749
  13. Venkatesh V., Morris M., Davis G., & Davis F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly. 27, 425-478. https://doi.org/10.2307/30036540
  14. Park, Y. M. et al. (2024). Application and usage condition analysis of smart hospital room automation system, Research Report. NHIMC-2023-PR-010, https://repository.nhimc.or.kr/bitstream/2023.oak/340/2/NHIMC-2023-PR-010.pdf
  15. Lee, H. C. B., & Wang, L. (2025). How costs influence Preferences for Control in Generative AI: Human-Guided vs. GenAI-Based Delegated Search. GenAI-Based Delegated Search (January 22, 2025).
  16. Ramesh, A. et al. (2021). Zero-shot text-to-image generation. In International conference on machine learning, 8821-8831.
  17. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. eprintarXiv:2204.06125, 1(2), 3. DOI : 10.48550/arXiv.2204.06125
  18. Betker, J. et al. (2023). Improving image generation with better captions. Computer Science. 2(3), 8. https://cdn.openai.com/papers/dall-e-3.pdf,
  19. Lee, C., & Kim, D. (2024). Enhancing education with ChatGPT 4o and Microsoft Copilot: A review of opportunities, challenges, and student perspectives on LLM-based text-to-image generation models. International Journal of Educational Technology, 29(1), 15-30.
  20. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 10684-10695.
  21. Wang, C., & Chung, J. (2023). Research on AI painting generation technology based on the [Stable Diffusion]. International journal of advanced smart convergence, 12(2), 90-95. DOI : 10.7236/IJASC.2023.12.2.90
  22. comfyanonymous. (2023). ComfyUI: Node-based GUI for Stable Diffusion. GitHub repository. https://github.com/comfyanonymous/ComfyUI
  23. Podell, D. et al. (2023). SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952.
  24. Stability AI. (2024). Stable Diffusion 3.5 Medium Model Card. AI-Science (Online). https://huggingface.co/stabilityai/stable-diffusion-3.5-medium
  25. Kim, H. (2024). Status of employment in the software industry: Total number of employees. SWSTAT (Software Industry Statistics Portal), SW Policy and Talent Research Division.
  26. Ministry of Trade, Industry and Energy, & Korea Institute of Design Promotion. (2022). 2022 design industry statistics
  27. Choung, W. (2023). A Study on the legal issues of Chat GPT. Inha Law Research Institute, 26(4), 329-355. DOI : 10.22789/IHLR.2023.12.26.4.10
  28. Kim, C. W. (2019). A study on the necessity and implementation of electronic person for autonomous system, Justice, 171, 5-48. DOI : 10.29305/tj.2019.04.171.5
  29. Yoon. H. R., Lee. H. I., Baek. B. S., Jung E. B., & Jeon. S. J. (2024). Measuring the user burden of UX practitioners using generative AI tools: Comparison of experience in using by annual practitioners. Proceedings of HCI Korea, 1,140-1,145.