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Empowering Teachers: Gen AI-Powered Auto Grading

Category:
Gen AI / EdTech

The Problem Statement

Educators manually review and grade all student answers, making the process time-consuming, error-prone, and difficult to scale. As the number of students grows, this manual approach increases the workload on educators, resulting in delays in providing feedback and inconsistencies in evaluation standards. The lack of timely and consistent feedback hampers students’ ability to identify mistakes and improve.   

The Solution

To overcome the challenges of manual grading, scalability, and delays in feedback in the online exam portal, we proposed a Gen AI-powered auto-grading system deployed on the cloud. This solution utilizes advanced Generative AI to automatically evaluate and score students’ written responses, minimizing the need for manual teacher intervention. 

The system is designed to adhere to AI ethics principles, ensuring fairness, transparency, and accountability in the grading process. By incorporating bias detection mechanisms and continuous model monitoring, the solution aims to deliver unbiased, equitable scoring for all students, regardless of their background, writing style, or language proficiency.  

Key Components

  1. Automated Evaluation Using Gen AI  
    1. Students’ written answers are passed through a Gen AI model trained to understand the question context, rubrics, and expected responses. 
    2. The model performs semantic analysis, content matching, and language quality checks to assign grades based on predefined criteria. 
    3. Partial credit and alternative valid answers can also be handled through Gen AI’s language understanding capabilities. 
  2. Customizable Grading Rubrics 
    1. Grading guidelines, marking schemes, and sample answers are referenced from the database. 
    2. The Gen AI model aligns evaluations with these guidelines, ensuring grading consistency across all student responses. 
  3. Feedback Generation 
    1. Along with grades, the system can generate personalized feedback explaining why a response was correct, partially correct, or incorrect. 
    2. Feedback is tailored to the individual student’s response, promoting better learning and engagement. 
  4. Human-in-Loop 
    1. Generated feedback and score is made available to the educator, where the educator can override the AI-generated feedback and grade as necessary.  
  5. Continuous Model Improvement 
    1. The system can learn from past grading data, improving accuracy over time by incorporating teacher reviews, manual overrides, and feedback loops. 
    2. Educators can also flag edge cases for human review, ensuring the system remains flexible and transparent. 

Value Add Delivered

  1. Scalability 
    1. The solution is deployed on a scalable cloud infrastructure, ensuring the system can handle thousands of simultaneous responses without performance degradation. 
    2. Cloud-based deployment also enables secure storage of exam data, compliance with educational data regulations, and seamless integration with existing online portals. 
  2. 80% reduction in manual grading 
  3. Faster feedback to the students 
  4. Consistent and Objective Grading 
  5. Scalability to Assess Thousands of Students Simultaneously 
  6. Improved Learning Outcomes through Detailed Feedback