Generative Knowledge Production Pipeline Driven by Academic Influencers

The preprint focuses on the paradigm of academic knowledge production, specifically addressing the integration of generative AI, particularly ChatGPT, into research workflows and publication processes. It examines the dynamic interplay between established academic integrity norms and the transformative potential of AI-driven tools, highlighting the role of academic influencers in shaping this evolving landscape.

The classification of the preprint as Model Drift/Model Crisis is based on the following observations:

1. The preprint acknowledges a shift in academic knowledge production due to generative AI. This shift, driven by readily available AI tools and promoted by academic influencers, is transforming research practices and workflows. This aligns with the concept of Model Drift, where existing models and practices begin to show strain under the pressure of new tools and techniques.

2. The study identifies a structured pipeline (Input, Process, Output) emerging from influencer tutorials. This pipeline represents an attempt to adapt existing academic norms to incorporate AI, suggesting an effort to manage the drift and prevent a full-blown crisis.

3. Despite the structured approach, the preprint highlights critical concerns about ethical compliance, quality control, and the potential for bias and misinformation in AI-generated content. These concerns point towards a Model Crisis, where the existing paradigm of academic integrity is challenged by the limitations and potential misuse of AI tools.

4. The preprint does not propose a complete overhaul of academic practices but emphasizes the need for further refinement and validation mechanisms within the AI-augmented workflow. This indicates an attempt to refine the existing model rather than replace it, placing the work within the drift/crisis phase.

5. The study focuses on the influence of academic social media influencers who are seen to fill the gaps left by formal institutional policies, accelerating the shift towards AI adoption in research, which may be uncontrolled. This further supports the categorization of Model Drift/Model Crisis.

While the preprint identifies the transformative impact of AI, it does not propose a radical departure from existing academic norms. The focus is on adapting current practices to integrate AI responsibly, addressing the challenges and opportunities presented by this technological advancement within the existing framework. Therefore, the preprint's stage is best characterized as a transition between Model Drift and Model Crisis, as it simultaneously acknowledges adaptation and the potential destabilization of traditional norms.