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Headquartered in the United States, our client is a leading provider of educational technology platforms designed to help students enhance their writing and grammar skills. The platform focuses on providing personalized, adaptive learning experiences for K-12 students through engaging and relevant content.
Education
A project aimed at automating the evaluation of student test responses using advanced language models, ensuring accurate and consistent grading. The solution is scalable, easily adapts to different educational environments, and requires minimal manual intervention.
The client wanted to implement an automated solution for evaluating student test responses. Their goal was to develop a comprehensive evaluation system that would provide accurate and consistent grading across diverse educational environments.
To achieve this, they sought a unified solution leveraging advanced Al techniques that would automate grading, alleviate teacher workloads, and offer prompt feedback to students.
We developed an automated grading solution using OpenAl's advanced language models, GPT-3.5 and GPT-4, to accurately and efficiently evaluate student test responses. Despite initial considerations to use open-source LLMS to reduce costs, we chose OpenAl's models, largely due to its superior accuracy and reliability.
To meet business requirements, we developed a solution that serves as a single package for evaluating student answers, assigning grades, and providing individual feedback. The system employs custom prompt engineering techniques to evaluate student responses in parts. This modular approach allowed us to methodically evaluate various aspects of the answers, ensuring a thorough and detailed evaluation. The Gradio app shows the final grade with grade breakdown.
The client wanted to implement an automated solution for evaluating student test responses. Their goal was to develop a comprehensive evaluation system that would provide accurate and consistent grading across diverse educational environments.
To achieve this, they sought a unified solution leveraging advanced Al techniques that would automate grading, alleviate teacher workloads, and offer prompt feedback to students.
We developed an automated grading solution using OpenAl's advanced language models, GPT-3.5 and GPT-4, to accurately and efficiently evaluate student test responses. Despite initial considerations to use open-source LLMS to reduce costs, we chose OpenAl's models, largely due to its superior accuracy and reliability.
To meet business requirements, we developed a solution that serves as a single package for evaluating student answers, assigning grades, and providing individual feedback. The system employs custom prompt engineering techniques to evaluate student responses in parts. This modular approach allowed us to methodically evaluate various aspects of the answers, ensuring a thorough and detailed evaluation. The Gradio app shows the final grade with grade breakdown.