Comparative study maps university AI-plagiarism governance in China and Japan

A comparative policy study of Chinese and Japanese higher education finds that institutional silence in China contrasts with a structured “No Copy + Exceptions” approach in Japan for addressing plagiarism involving generative artificial intelligence. The analysis examined the Japanese government’s response and policies at 359 universities, selected because of their systemic and institutional similarities to Chinese counterparts. It reports that the Chinese government had expressed concern about the issue but had not taken formal action, while many Chinese institutions maintained a position of silence.

Plagiarism involving generative-AI content has resurfaced with greater intensity and evasiveness as the technology becomes more integrated into higher education, according to the study. The authors note that no definitive consensus exists on the precise definition of “AIGC-plagiarism.” That conceptual ambiguity, however, has not prevented the practice from being broadly recognized as prohibited academic conduct. Their analysis therefore treats the central issue as a governance problem: how national and institutional systems should respond when established academic-integrity rules are confronted by a rapidly changing technology.

Japan’s response was characterized as “No Copy + Exceptions,” a framework in which copying is prohibited as the baseline while specified circumstances permit exceptions. The study presents this approach as particularly instructive for Chinese higher education. It does not report that the policy reduced plagiarism or establish the prevalence of generative-AI misuse. Instead, it compares regulatory strategies and argues that Japan’s combination of a general restriction with exceptions offers a more active institutional response than continued silence.

The authors identify technological and political factors as contributors to China’s regulatory inertia. They characterize silence as a pragmatic strategy under technological uncertainty but also as an unacceptable form of de facto endorsement or passive acquiescence. Their proposed response includes a unified national benchmark and implementation rule, a dynamic structure for assessing AI’s impact, autonomy for faculty and academic councils, and mechanisms that allow students to participate and provide feedback. These recommendations are presented as components of a more proactive, responsive and agile governance system rather than as outcomes demonstrated by an intervention study.

The research is a comparative policy analysis, not an experiment involving students, universities or AI-detection software. It does not establish how many students use generative AI, whether particular detection technologies can reliably classify generated content, or whether Japan’s rules have changed misconduct rates over time. Its scope is likewise limited to the higher-education governance responses being compared. Within those boundaries, the central finding is a contrast between institutional inaction and a formally structured exception-based approach, supported by an argument that universities need adaptable mechanisms rather than prolonged silence as generative AI becomes more deeply embedded in academic work.

Subject of Research: Comparative analysis of generative-AI plagiarism governance in Chinese and Japanese higher education

Article Title: AI-generated content-plagiarism versus institutional silence: comparative insights on responses of higher education to rapid technological changes in China and Japan

Article References: Zhang, T., & Chen, H. (2026). AI-generated content-plagiarism versus institutional silence: comparative insights on responses of higher education to rapid technological changes in China and Japan. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09431-x

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09431-x

Keywords: generative AI, academic plagiarism, higher education, China, Japan, university policy, academic integrity, AI governance, comparative study, AI-generated, content-plagiarism, versus