Automated Essay Grading Workflow with AI Integration

Automated Essay Grading and Feedback Generation

1. Essay Submission

1.1 Student Submission

Students submit their essays through an online platform, such as Google Classroom or Moodle.

1.2 Document Formatting

Essays are automatically formatted for consistency using tools like Grammarly or Microsoft Word’s built-in formatting features.

2. Initial Text Analysis

2.1 AI Text Analysis Tool Integration

Utilize AI-driven summarizer tools such as OpenAI’s GPT-3 or IBM Watson to perform an initial analysis of the essay content.

2.2 Key Metrics Extraction

Extract key metrics such as word count, readability score, and grammatical accuracy using tools like Hemingway Editor or Readable.

3. Grading Criteria Establishment

3.1 Define Grading Rubric

Establish a clear grading rubric that includes criteria such as thesis statement clarity, argument strength, organization, and grammar.

3.2 AI Grading Tool Selection

Select an AI grading tool, such as Turnitin’s Revision Assistant or ETS’s e-rater, that aligns with the established grading rubric.

4. Automated Grading Process

4.1 AI Grading Implementation

Deploy the selected AI grading tool to evaluate the essays based on the predefined rubric.

4.2 Feedback Generation

Utilize the AI tool’s feedback capabilities to generate detailed comments on strengths and areas for improvement.

5. Review and Adjustment

5.1 Instructor Review

Instructors review the AI-generated grades and feedback to ensure accuracy and fairness.

5.2 Adjustments and Re-grading

If necessary, instructors can adjust grades or feedback before finalizing the results.

6. Feedback Distribution

6.1 Student Access

Distribute the finalized grades and feedback to students through the online platform.

6.2 Follow-Up Sessions

Schedule follow-up sessions for students to discuss feedback and areas for improvement.

7. Continuous Improvement

7.1 Data Analysis

Analyze data from multiple submissions to assess the effectiveness of the AI grading system and feedback quality.

7.2 System Refinement

Refine grading rubrics and AI tool parameters based on data analysis to improve future grading accuracy and feedback relevance.