A Human-in-the-Loop Framework for Reviewing AI-Generated Research Articles

Published by: Latent Scholar LLC | Author: Amin Riazi

AI-generated content seems to be everywhere now. Large language models are now producing articles, reports, and even research papers that read like human-written texts. However, AI emulative texts, and just because something sounds authoritative does not guarantee its authenticity and accuracy.

I’ve been thinking about the challenge of dealing with AI-generated texts for a while. As someone working at the intersection of AI and publishing, I’ve seen how AI-generated texts contain subtle errors, unsupported claims, and outright fabrications—alluding to “hallucinations.” Without proper verification, these issues can slip into the scholarly records, get cited by other researchers, and thus spread misinformation.

Accordingly, I’m presenting a new framework developed at Latent Scholar LLC, which may tackle this issue. A structured system for creating, reviewing, and publishing fully AI-generated articles with human expert review and reflection. This article outlines the framework and serves as a public record for this new initiative.

The Problem We’re Facing

Current academic publishing workflows are not designed for AI-authored content. There is an assumption that a human has authored the paper based on the research they conducted, and that the author stands behind the claims made in the paper. Now, let’s think of the scenario where an AI has generated the article. The critical question then is who is responsible for verifying the content and the claims made in the paper?

Right now, nobody! There are indeed some detecting tools claiming to detect AI-generated text. However, AI-generated articles can be published without systematic reviews and evaluation. They can enter databases, get indexed, and become citable sources. Once that happens, unwarranted claims and subtle errors will be embedded in the scholarly literature. Other researchers will cite them, build on them, and this may enter into a vicious cycle.

We thus need a better approach—one that harnesses AI’s ability to generate content while ensuring human experts verify authenticity and accuracy of such texts before they get published.

The Proposed Solution

This article presents a four-step process that combines AI generation with human supervision:

Idea Submission. An expert human contributor proposes an idea, a research topic, or a question. This ensures that the subsequently produced article, using the idea, topic, or question, addresses a genuine need and has a real person accountable for the initially proposed idea.
AI Generation. The system takes the submitted idea, formulates an optimized prompt using relevant parameters, and generates a full article through a large language model. Depending on the topic, the output includes standard sections of an academic article, such as title, abstract, introduction, methodology, results, discussion, conclusion, and references.
Automated Pre-Checks. Before the generated text goes to human review, the system runs preliminary automated verifications: plagiarism detection and citation and reference accuracy checks.
Human Expert Review. Subject-matter experts volunteer to review the AI-generated article, looking for biases, hallucinations, and potential content, language, and structural errors. They provide a full review that is published alongside the article, so that readers can see exactly the strengths of the article and authentic content, and the concerns raised.

The key element here is transparency. Both the AI-generated article and the expert review are published together. Readers can distinguish between authenticated content and unwarranted content.

How the System Works

1. The Idea Submission Module

Contributors submit an idea for a paper through a dedicated interface. The system captures key metadata: the contributor’s name, submission date, research field, the brief description, and relevant keywords. This creates accountability and traceability from the start.

2. The AI Generation Module

The submitted idea gets converted into a structured prompt optimized for academic writing. A large language model then generates the full article. The system works with any capable LLM, making it adaptable as AI technology evolves.

3. Automated Pre-Checks

Before the article is made visible, automated systems flag potential issues:

Plagiarism detection identifies exact text overlaps from existing sources without appropriate citations
Citation verification checks whether cited sources exist, and if so, whether they are accurate

These preliminary automated checks do not replace human judgment—they are intended to reduce experts’ workload so that they can focus on the most important aspects, such as the authenticity of the content and claims.

4. Human Expert Review

This is where the real review happens. Subject domain experts evaluate the article across several dimensions:

Factual accuracy: Are the claims supported by evidence?
Bias detection: Does the article present information fairly and a balanced view, or does it skew toward particular conclusions?
Hallucination identification: Has the AI fabricated facts, citations, or data?
Scientific rigor: Does the methodology make sense? Are the conclusions warranted?

Reviewers provide a detailed review, highlighting concerns, severity levels, and suggested corrections. Reviewers’ review gets published, creating a permanent record of what was reviewed and what reflections were made.

Why This Matters

AI is not going away and will stay with us! In fact, it is going to play an increasingly critical role in content creation, including academic and research writing. The question, therefore, is not whether AI will generate research articles—it already does. The question is whether there are systems in place to ensure that the AI-generated content is reliable and valid.

This framework provides a concrete implementation of such a system. The framework channels AI-generated content through a verification process that can maintain scholarly integrity. By publishing human reviews alongside the AI-generated articles, the system gives readers the information they need to evaluate and develop judgment about the text they are reading.

Possible Variations and Extensions

The proposed framework is designed to be flexible. Some potential variations are:

Multiple review rounds, with giving AI the opportunity to revise the initially generated text using human reviews and feedback
Multiple expert reviews instead of single reviewers. This will allow multiple perspectives and more comprehensive reviews.
Domain-specific verification modules (chemical formula validation, statistical checks, engineering calculations)
Integration with existing academic publishing platforms
Different interfaces: web-based, mobile apps, or desktop applications
Factual consistency checks cross-reference key claims against trusted databases

Moving Forward

This framework is publicly disclosed to address a real, timely, and growing issue. As AI becomes more capable, the need for systematic authentication of AI-generated texts will only increase. By establishing a systematic approach now, we can set standards for responsible AI authorship before the problem becomes unmanageable.

This article constitutes a public disclosure of this method and its essential concepts as of December 5, 2025.