Generative Knowledge Production Pipeline Driven by Academic Influencers

ABSTRACT Generative AI transforms knowledge production, validation, and dissemination, raising academic integrity and credibility concerns. This study examines 53 academic influencer videos that reached 5.3 million viewers to identify an emerging, structured, implementation-ready pipeline balancing originality, ethical compliance, and human-AI collaboration despite the disruptive impacts. Findings highlight generative AI's potential to automate publication workflows and democratize participation in knowledge production while challenging traditional scientific norms. Academic influencers emerge as key intermediaries in this paradigm shift, connecting bottom-up practices with institutional policies to improve adaptability. Accordingly, the study proposes a generative publication production pipeline and a policy framework for co-intelligence adaptation and reinforcing credibility-centered standards in AI-powered research. These insights support scholars, educators, and policymakers in understanding AI's transformative impact by advocating responsible and innovation-driven knowledge production. Additionally, they reveal pathways for automating best practices, optimizing scholarly workflows, and fostering creativity in academic research and publication. Keywords: generative AI, ChatPGT, academic integrity, influencers, knowledge production, social media, policy implications, academic policy 1. INTRODUCTION The advent of generative AI (GenAI) transforms knowledge production, increasingly supporting and partially automating the academic workflow (Bolanos et al. 2024). This trend suggests a paradigm shift where researchers utilize effectively and productively generative AI tools, potentially leading to more automated scientific workflows. However, we have also identified a human component in this process: the impact of the academic influencers via social media promoting hands-on knowledge about GenAI in academic projects.