The Attention Mismatch: Mapping the Structural Academic Governance Deficit in the Age of Generative AI

With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, 2013–2026), and bibliometric mapping of governance scholarship (Web of Science Core Collection, Scopus, Google Scholar, 2020–2026) to diagnose the cross-level misalignment between synthetic-content diffusion, AI-related misconduct pressure, and governance attention. On this basis, it proposes a Normalized Coverage Index (NCI) to measure the relative relationship between scholarly attention to AI-related academic misconduct governance and the level of misconduct pressure observed through retraction data across disciplines. The results reveal pronounced asymmetries at the disciplinary level. Fields such as chemistry (0.04), physics, mathematics & statistics (0.11), and life sciences & biology (0.34) exhibit clear governance gaps, whereas Education shows a comparatively excessive level of attention (NCI = 29.26). Since 2022, AIGC has expanded rapidly across open web corpora, accompanied by a sharp rise in AI-related retractions, which also exhibit a longer detection lag than traditional forms of misconduct (2.77 years vs. 1.91 years). Although the volume of academic governance-related research has grown rapidly, its proportion within the broader body of AI-related research has declined, suggesting that scholarly attention to governance has not kept pace with technological diffusion. Consequently, a structural misalignment in governance—closely tied to the allocation of attention—has emerged within the academic system in the era of GenAI. This misalignment may pose potential risks to the robustness of the knowledge production system. Addressing it requires rebuilding epistemic infrastructure through provenance transparency, auditable workflows, and governance-aware seed corpora aligned with empirically concentrated risks.