From Uncontrolled Artificial Generation to an Accountable Research Partnership: Methodological Governance of LLMs in Academic Work

Large Language Models (LLMs) are increasingly integrated into academic workflows, presenting both opportunities and significant challenges to scholarly integrity. While issues of factual accuracy are widely discussed, a more subtle and dangerous failure mode is structural hallucination, where LLM-generated content, despite being factually correct at a sentence level, distorts the relational structure of knowledge, misrepresents bibliographic landscapes, and undermines intellectual attribution. This paper argues for a shift from treating LLMs as uncontrolled artificial generators to incorporating them into an accountable research partnership. We propose a researcher- and instructor-centric governance framework based on a suite of lightweight, individually implementable computational protocols. By combining knowledge graph extraction with social network and bibliometric analysis, our methodology provides a transparent, quantitative toolkit for validating the structural and bibliographic integrity of LLM-assisted work. We demonstrate how network diagnostics, including centrality analysis and modularity, can serve as a ``hallucination stress test'' to detect conceptual distortions. We further detail protocols for citation integrity and bibliometric benchmarking to ground LLM outputs in real scholarly ecosystems. The paper outlines direct applications of this framework in high-stakes academic practices, including manuscript writing, syllabus design, and student assessment. Ultimately, we argue that methodological governance, rather than outright prohibition, is the most effective path toward a responsible and productive human-LLM collaboration in academia.