Generative Knowledge Production Pipeline Driven by Academic Influencers
It addresses academic integrity and credibility concerns in AI-driven knowledge production for scholars, educators, and policymakers, but is incremental as it builds on existing discussions of AI in academia.
This study analyzed 53 academic influencer videos to identify a generative AI-driven pipeline for knowledge production that balances originality, ethics, and human-AI collaboration, reaching 5.3 million viewers and proposing a framework for automating publication workflows and enhancing credibility.
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.