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

This study seeks to elucidate how the higher education systems of China and Japan have addressed the issue of plagiarism exacerbated by generative artificial intelligence (AIGC-plagiarism) and the insights gained from these responses. As generative AI becomes increasingly integrated with higher education, the age-old problem of plagiarism has resurfaced with greater intensity and elusiveness, sweeping across universities worldwide. Although no definitive consensus exists regarding the precise definition of AIGC-plagiarism, this conceptual ambiguity does not preclude its broad recognition as prohibited academic conduct. The relationship between cutting-edge technologies and higher education appears to have reached an impasse, yet the majority of Chinese institutions have maintained a stance of silence, while the Chinese government has shown concern but refrained from taking formal action. This research comparatively examines the response strategies to AIGC-plagiarism by the Japanese government and 359 universities, which share similar systemic and institutional contexts with their Chinese counterparts, and reaches a conclusion that the practice of “No Copy + Exceptions” emerges as a particularly instructive approach. A micro-level comparative analysis reveals that this regulatory inertia is shaped by both technological and political factors, and represents a pragmatic strategy in the face of technological uncertainty. Regardless of the underlying rationale, such silence constitutes an unacceptable form of de facto endorsement or passive acquiescence. Higher education systems must develop more proactive, responsive, and agile mechanisms to cultivate morally anchored, innovation-capable students amidst technological and geopolitical turbulence. Key components of this mechanism include, but are not limited to a unified national benchmark and implementation rule, a dynamic AI impact assessment structure, autonomy of faculty and academic councils, and student participation with feedback channels.