AI-related Copyright Awareness Among Game Majors: A Cross-national Study in Vietnam and South Korea
DOI:
https://doi.org/10.13052/jwe1540-9589.2481Keywords:
Statistical analysis, principal component analysis (PCA), copyright technology, scree plot, exploratory factor analysis, Python, Jupyter NotebookAbstract
The rapid advancement of artificial intelligence (AI) has brought a paradigm shift to creative practices across various content industries, including the game industry. In particular, the application of AI technologies in areas such as character generation, storytelling, and level design has become increasingly prevalent in game development. As the use of AI-generated content expands, debates concerning its legal status and copyright protection have gained significant momentum. Understanding how future professionals perceive these issues is critical, especially in fields where AI-based creation is directly applied.
In this study, we analyzed and compared the level of awareness regarding copyright technologies and related legal issues in AI-based game development among approximately 450 university students majoring in game-related disciplines in Vietnam and South Korea. By taking into account the cultural and institutional contexts of the two countries, we identified both commonalities and differences in students’ perceptions and attitudes. For data analysis, a mathematical pattern analysis approach was applied to examine inter-item correlations and factor structures, with results visualized using Python in the Jupyter Notebook environment. Furthermore, Bartlett’s test of sphericity was conducted to verify the statistical suitability of the dataset for factor analysis, confirming significant correlations among the survey items.
Downloads
References
J. C. Ginsburg and L. Budiardjo, “Authors and machines,” Columbia Public Law Research Paper, no. 14-612, 2019. [Online]. Available: https://doi.org/10.2139/ssrn.3476906.
A. Elgammal, B. Liu, M. Elhoseiny, and M. Mazzone, “Can AI create art? Learning-based art generation, the case of AICAN,” J. Vis. Cult., vol. 19, no. 1, pp. 45–67, 2020.
P. Samuelson, “Allocating ownership rights in computer-generated works,” Univ. Calif. Law Rev., vol. 108, no. 3, pp. 801–854, 2020.
S. Zhou, M. Lee, and R. Thomas, “AI in plagiarism detection: A systematic review of tools and trends,” IEEE Trans. Learn. Technol., vol. 14, no. 1, pp. 78–92, 2021. [Online]. Available: https://doi.org/10.1109/TLT.2021.3054102.
Y. Jang, J. Kim, H. Lee and Y. Seo, “Generative AI-driven Graphic Pipeline for Web-based Editing of 4D Volumetric Data,” Journal of Web Engineering. Vol. 24, no. 1, pp. 135–162, 2025.
K. Shim, B. Kim and W. Lee, “Research on Quantum Key, Distribution Key and Post-quantum Cryptography Key Applied Protocols for Data Science and Web Security,” Journal of Web Engineering. Vol. 23, no. 6, pp. 813–830, 2024.
Y. Kim and D. Lee, “AI-generated music and copyright law in Korea: Legal challenges and future directions,” Seoul Law Rev., vol. 43, no. 2, pp. 201–225, 2021.
H. Ito and J. Kim, “Digital literacy and legal awareness in East Asian tech students: A cross-national comparison,” Asian Educ. Rev., vol. 19, no. 1, pp. 56–72, 2021.
K. Park, M. Choi, and H. Shin, “Perceptions of AI and ownership among Korean engineering students,” J. Eng. Ethics Educ., vol. 13, no. 2, pp. 88–105, 2021.
S. Lee and T. Yamada, “Comparing AI ethics education in Korea and Japan: Institutional approaches and student perceptions,” J. Comp. Technol. Educ., vol. 16, no. 3, pp. 101–117, 2022.
M. T. Tran, “AI and copyright challenges in Vietnam: A review of legal gaps and enforcement needs,” Vietnam J. Law Technol., vol. 11, no. 3, pp. 32–45, 2023.
R. Abbott, “The reasonable robot: Artificial intelligence and the law,” Cambridge Univ. Press, 2020.
H. Surden, “Artificial intelligence and law: An overview,” Ga. State Univ. Law Rev., vol. 35, no. 4, pp. 1305–1338, 2019.
B. Hugenholtz, “Copyright and artificial creativity: Why copyright cannot fix the problem,” IIC, vol. 51, pp. 1001–1020, 2020.
S. Stokes, “Art and copyright in the age of AI,” J. Intellectual Property Law & Practice, vol. 16, no. 9, pp. 793–801, 2021.
W. Liu, “Who owns AI-generated content? Comparing emerging models in China, UK, and US,” J. Copyright Soc., vol. 68, no. 2, pp. 123–152, 2021.
A. S. Narayanan and S. M. Rubin, “Machine learning for copyright enforcement,” ACM Comput. Surveys, vol. 54, no. 3, pp. 1–39, 2022.
L. Liu, J. Wang, and X. Xu, “A CNN-based plagiarism detection model with attention mechanism,” Expert Syst. Appl., vol. 176, 2021.
T. Zhang and M. Li, “Detecting visual infringement in AI-generated media using GAN features,” Pattern Recognit., vol. 129, 2022.
B. Koops, “The trouble with algorithmic enforcement,” Comput. Law Rev. Int., vol. 21, no. 3, pp. 73–80, 2020.
N. Eidelman and S. Sarid, “Do students understand AI copyright? A case study in Israeli CS education,” Int. J. Technol. Educ., vol. 13, no. 2, pp. 88–102, 2021.
J. Chen, Y. Huang, and C. Lin, “University students’ attitudes toward AI-generated content and IP rights,” J. Ethics Inf. Technol., vol. 23, no. 4, pp. 389–403, 2021.
M. Ahmad, K. Kwan, and S. Nguyen, “Cross-cultural analysis of AI authorship attitudes among ASEAN students,” Asian J. Digit. Rights, vol. 5, no. 1, pp. 51–70, 2023.
Bartlett, M. S. (1954). A note on the multiplying factors for various chi-square approximations. Journal of the Royal Statistical Society: Series B (Methodological), 16(2), 296–298.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.
