Overview of Rubric-Linked AI Review Tool

The Rubric-Linked AI Review Tool is an innovative software module designed to streamline and enhance the feedback process for developers by leveraging artificial intelligence (AI) in alignment with custom grading rubrics. This tool bridges the gap between subjective human evaluations and objective, data-driven insights, ensuring consistent and actionable feedback tailored to specific project requirements.

Purpose

The primary purpose of this module is to automate the review and feedback generation process while maintaining strict adherence to predefined grading criteria. By integrating AI algorithms with customizable rubrics, the tool provides developers with detailed, accurate, and context-aware feedback that goes beyond traditional grade-based evaluations. This enables users to identify areas for improvement, optimize their workflows, and enhance the overall quality of their work.

Benefits

The Rubric-Linked AI Review Tool offers several key benefits:

• Customizable Grading: Users can define and customize rubrics according to their specific project needs, ensuring that feedback aligns with unique evaluation criteria.
• Efficiency: Automates the review process, saving time while maintaining high standards of quality and consistency.
• Accuracy: Leverages AI to analyze code or other text-based content thoroughly, reducing errors and biases inherent in manual reviews.
• Detailed Insights: Provides granular feedback on specific aspects of the work being reviewed, highlighting strengths and areas for improvement.
• Scalability: Capable of handling large volumes of data and multiple projects simultaneously, making it suitable for both small-scale and enterprise-level use cases.

Usage Scenarios

The Rubric-Linked AI Review Tool is ideal for various scenarios, including:

1. Code Reviews: Developers can use the tool to evaluate code quality, adherence to best practices, and compliance with project-specific guidelines.
2. Academic Assessments: Instructors or teaching assistants can apply this tool to grade student assignments, papers, or projects based on predefined rubrics.
3. General Document Review: Beyond code, the tool can analyze written content such as technical documentation, research papers, or proposals to ensure clarity, coherence, and alignment with specified criteria.
4. Collaborative Environments: Teams can leverage this tool to maintain consistent standards across multiple contributors, fostering collaboration and knowledge sharing.

By incorporating the Rubric-Linked AI Review Tool into their workflow, developers can significantly improve the efficiency, accuracy, and effectiveness of their review processes while ensuring that feedback is always aligned with custom grading rubrics.

Features of Rubric-Linked AI Review Tool

1. Rubric Integration

The tool allows seamless integration with custom grading rubrics, enabling developers to define specific criteria for code reviews. This ensures feedback aligns precisely with project requirements.

2. AI-Powered Feedback Generation

Leverages advanced NLP models to analyze source code and provide context-aware feedback, identifying areas for improvement based on predefined rubric standards.

3. Customizable Rubric Editor

Includes a user-friendly editor that supports drag-and-drop functionality, making it easy to create, modify, and manage custom rubrics tailored to specific coding projects.

4. Real-Time Feedback Generation

Delivers immediate feedback as developers write code, highlighting issues in real-time based on the selected rubric criteria to guide instant improvements.

5. Collaboration Features

Facilitates teamwork by allowing multiple users to review and comment on feedback within the platform, enhancing collective code quality through collaborative discussions.

6. Comprehensive Reporting

Generates detailed reports on review outcomes, including metrics and trends over time. These reports can be exported in formats like CSV or PDF for further analysis and presentations.

7. Version Control Integration

Integrates with version control systems (e.g., Git) to track how feedback evolves alongside code changes, providing a historical perspective on improvements and issues.

8. Scalability and Performance

Optimized for large-scale projects with features like load balancing and asynchronous processing to ensure efficient performance even under high user loads or extensive codebases.

9. Extensibility and Hooks

Offers customization options through hooks, allowing developers to integrate additional functionalities without altering the core module, thus preserving its stability and scalability.

10. Security and Compliance

Ensures data security with encryption and role-based access control (RBAC), safeguarding sensitive information and adhering to compliance standards, making it suitable for enterprise environments.

These features collectively enhance the efficiency, accuracy, and adaptability of code reviews, empowering developers to deliver high-quality software consistently.

Here’s the technical documentation for the Rubric-Linked AI Review Tool:

1. FastAPI Endpoint

This endpoint accepts a review request along with a rubric ID and returns feedback aligned with the specified rubric.

from fastapi import APIRouter, Depends, HTTPException
from typing import Any
from pydantic import BaseModel

router = APIRouter()

class ReviewRequest(BaseModel):
    rubric_id: str
    text: str

@router.post("/review")
async def review_text(
    request: ReviewRequest,
) -> dict:
    """
    Reviews text based on the specified rubric.
    Args:
        request (ReviewRequest): Contains rubric ID and text to review
    Returns:
        dict: Feedback aligned with the custom grading rubric
    """
    try:
        # Simulated processing - replace with actual AI processing
        feedback = {
            "rubric": {
                "id": request.rubric_id,
                "criteria": ["Clarity", "Depth", "Relevance", "Structure"]
            },
            "scores": {
                "Clarity": round((5 + 1) * random.random()),
                "Depth": round((5 + 1) * random.random()),
                "Relevance": round((5 + 1) * random.random()),
                "Structure": round((5 + 1) * random.random())
            },
            "comments": {
                "Clarity": f"Clarity score: {feedback['scores']['Clarity']}/5",
                "Depth": f"Depth score: {feedback['scores']['Depth']}/5", 
                "Relevance": f"Relevance score: {feedback['scores']['Relevance']}/5",
                "Structure": f"Structure score: {feedback['scores']['Structure']}/5"
            }
        }
        
        return feedback
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

2. React UI Snippet

This React component provides a simple interface for submitting text for review and displaying rubric-aligned feedback.

import React, { useState, useEffect } from 'react';

interface Feedback {
    rubric: {
        id: string;
        criteria: string[];
    };
    scores: Record<string, number>;
    comments: Record<string, string>;
}

export const ReviewForm = () => {
    const [text, setText] = useState("");
    const [feedback, setFeedback] = useState<Feedback | null>(null);

    const handleSubmit = async (e: React.FormEvent) => {
        e.preventDefault();
        try {
            const response = await fetch('/api/review', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json',
                },
                body: JSON.stringify({
                    rubric_id: "1",
                    text: text
                }),
            });

            if (!response.ok) {
                throw new Error('Failed to get feedback');
            }

            const data = await response.json();
            setFeedback(data);
        } catch (error) {
            console.error('Error:', error);
        }
    };

    return (
        <div className="review-container">
            <form onSubmit={handleSubmit}>
                <textarea 
                    value={text}
                    onChange={(e) => setText(e.target.value)}
                    placeholder="Enter text for review..."
                    className="review-textarea"
                    rows={4}
                />
                <button type="submit" className="review-button">Analyze</button>
            </form>

            {feedback && (
                <div className="feedback-container">
                    <h3>Feedback:</h3>
                    {feedback.rubric.criteria.map((criterion) => (
                        <div key={criterion} className="criteria-item">
                            <p><strong>{criterion}:</strong></p>
                            <p>{feedback.comments[criterion]}</p>
                        </div>
                    ))}
                </div>
            )}
        </div>
    );
};

3. Pydantic Data Schema

This schema defines the structure for review requests and feedback responses.

from pydantic import BaseModel
from typing import Dict, List, Optional

class ReviewRequest(BaseModel):
    rubric_id: str
    text: str

class FeedbackResponse(BaseModel):
    rubric: Dict[str, Any]
    scores: Dict[str, int]
    comments: Dict[str, str]

# Example usage:
# request = ReviewRequest(
#     rubric_id="1",
#     text="This is the text to be reviewed."
# )

Explanation

• FastAPI Endpoint: This endpoint handles incoming review requests, processes them using a simulated AI model (in this example), and returns structured feedback based on the specified rubric. It includes error handling and proper request validation.

• React UI Snippet: This component provides a user-friendly interface for submitting text for review and displaying the feedback. It uses React hooks for state management and effect handling, and it makes API calls to the FastAPI endpoint.

• Pydantic Data Schema: These models define the data structures used throughout the system, ensuring type safety and proper data validation both on the frontend and backend.

This documentation provides a comprehensive overview of how the Rubric-Linked AI Review Tool can be implemented using FastAPI for the backend, React for the frontend, and Pydantic for data modeling.

Technical Documentation for Rubric-Linked AI Review Tool

Overview

The Rubric-Linked AI Review Tool leverages custom grading rubrics to provide tailored feedback through artificial intelligence. Designed with developers in mind, this tool enhances review processes across various domains by integrating seamlessly into existing systems.

Related Modules

1. Rubric Definition Module: Manages the creation and maintenance of rubrics.
2. AI Feedback Engine: Processes feedback using AI based on defined rubrics.
3. User Interface Module: Facilitates user interaction for content submission and feedback retrieval.
4. Reporting Analytics Module: Provides insights into review performance and trends.

Use Cases

1. Academic Paper Review: Automates grading in educational settings, improving efficiency and consistency.
2. Software Code Quality Assessment: Enhances code reviews by applying custom quality metrics.
3. Business Proposal Evaluation: Streamlines proposal feedback using predefined business criteria.

Integration Tips

• API Usage: Utilize RESTful APIs for integration, ensuring efficient handling of requests and responses.
• Rubric Synchronization: Regularly update rubrics to maintain alignment with current standards.
• Data Privacy Compliance: Implement GDPR or CCPA compliance measures for data protection.
• Asynchronous Processing: Use background tasks to manage long-running feedback processes.
• Monitoring & Logging: Track performance metrics and logs for maintenance and optimization.

Configuration Options

Conclusion

The Rubric-Linked AI Review Tool offers a robust solution for developers seeking to integrate intelligent feedback systems. By leveraging predefined rubrics and advanced AI, it enhances review processes across diverse applications, ensuring efficiency and accuracy.