alihan/rigorous-ai_peer_review
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Academic Manuscript Peer Review Tool

This tool uses OpenAI's GPT-4 to perform automated peer reviews of academic manuscripts. It analyzes PDF manuscripts against a set of review criteria and provides detailed feedback, scores, and recommendations.

Features

  • Automated peer review of academic manuscripts
  • Comprehensive analysis across multiple review criteria
  • Detailed feedback with specific examples and suggestions
  • Metadata extraction and document structure analysis
  • Support for multiple PDF files
  • Configurable review criteria

Installation

  1. Clone the repository
  2. Install the required dependencies: 
    pip install -r requirements.txt
  3. Create a .env file in the root directory with your OpenAI API key: 
    OPENAI_API_KEY=your_api_key_here
    Note: The tool will look for the .env file in the current directory first, then in the parent directory.

Usage

  1. Place your PDF manuscripts in the manuscripts directory
  2. (Optional) Customize the review criteria in review_criteria.json
  3. Run the review tool: 
    python src/main.py --criteria review_criteria.json

Command Line Arguments

  • --manuscripts-dir: Directory containing PDF manuscripts (default: manuscripts)
  • --criteria: Path to the review criteria JSON file (required)
  • --output-dir: Directory to save review results (default: analysis_results)
  • --api-key: OpenAI API key (optional if set in environment)

Review Criteria

The tool evaluates manuscripts against the following criteria:

  1. Originality and Innovation
  2. Methodology
  3. Results and Analysis
  4. Writing and Presentation
  5. Technical Accuracy
  6. Literature Review
  7. Figures and Tables
  8. References
  9. Ethical Considerations
  10. Impact and Significance

Each criterion is scored on a scale of 1-5, with detailed feedback and specific examples provided.

Output Format

The review results are saved in text files with the following sections:

  • Manuscript Metadata
  • Document Statistics
  • Overall Assessment
  • Detailed Assessment (per criterion)
    • Score
    • Feedback
    • Examples
    • Suggestions for Improvement

Requirements

  • Python 3.7+
  • OpenAI API key
  • PDF manuscripts to review

License

This project is licensed under the MIT License - see the LICENSE file for details.