Generative Knowledge Production Pipeline Driven by Academic Influencers

TL;DR

This paper explores how academic influencers utilize generative AI to create a structured, ethical, and collaborative knowledge production pipeline that automates workflows and challenges traditional norms, with implications for policy and credibility.

Contribution

It introduces a novel generative publication pipeline and policy framework integrating AI-driven practices with academic norms and influencer roles.

Findings

Generative AI can automate publication workflows.

Academic influencers serve as key intermediaries.

The proposed pipeline balances originality, ethics, and collaboration.

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.