Automated Essay Grading Workflow with AI Integration

Automated Essay Grading and Feedback Workflow

1. Essay Submission

1.1 Student Submission

Students submit their essays through an online learning management system (LMS) such as Canvas or Moodle.

1.2 File Format Verification

The system checks the file format to ensure compatibility (e.g., PDF, DOCX).

2. Preprocessing of Essays

2.1 Text Extraction

Utilize Optical Character Recognition (OCR) tools like Adobe Acrobat to extract text from submitted documents.

2.2 Plagiarism Detection

Implement tools such as Turnitin or Grammarly to check for originality and provide a plagiarism report.

3. Automated Grading

3.1 AI-Driven Grading Tool Selection

Select an AI-powered grading tool, such as Gradescope or ETS’s e-rater, to evaluate the essays based on predefined rubrics.

3.2 Grading Criteria

The AI assesses various criteria including:

• Content relevance
• Structure and organization
• Grammar and syntax
• Style and tone

3.3 Score Generation

The AI tool generates a score based on the grading criteria and provides a detailed analysis of strengths and weaknesses.

4. Feedback Generation

4.1 Automated Feedback Creation

Utilize AI tools like QuillBot or ProWritingAid to generate personalized feedback based on the grading results.

4.2 Feedback Components

Feedback includes:

• Specific suggestions for improvement
• Examples of well-structured arguments
• Grammar corrections

5. Review and Revision

5.1 Student Access to Grades and Feedback

Students receive their grades and feedback through the LMS interface.

5.2 Revision Opportunity

Provide students with the chance to revise their essays based on the feedback received, encouraging iterative learning.

6. Final Submission

6.1 Resubmission Process

Students submit their revised essays for final evaluation through the LMS.

6.2 Final Grading

Re-evaluate the revised essays using the same AI grading tools to provide a final score and feedback.

7. Data Analysis and Improvement

7.1 Aggregate Data Collection

Collect data on essay scores and feedback to analyze trends and areas for curriculum improvement.

7.2 Continuous Improvement

Utilize insights from data analysis to refine grading rubrics and feedback mechanisms, ensuring the AI tools evolve with educational needs.