AI-Powered Teacher Assistant: Automated Grading and Personalized Feedback
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2026
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| author | Harshal Shriram Patil Shruti Vikas Badgujar Sanika Satish Patil Aakash Anil Dhamane Payal Bhikanrao Patil |
| author_facet | Harshal Shriram Patil Shruti Vikas Badgujar Sanika Satish Patil Aakash Anil Dhamane Payal Bhikanrao Patil |
| contents | This is an artificial intelligence-based teaching assistant that makes the grading process easier and provides students with specific and personalized feedback. With the use of Optical Character Recognition (OCR) and natural-language processing (NLP), the system is capable of processing both handwritten and electronic submissions, requiring a significantly smaller amount of manual work to carry out the process of assessment. Using deep-learning algorithms (trained on TensorFlow and PyTorch) the assistant derives semantic meaning in response to the students and provides explicit and practical recommendations of what to do better. The solution is hosted on scalable cloud infrastructure, ensuring there is minimal latency and maximum availability even when all classrooms are in use. The system is developed using Python as the backend logic and a user-friendly interface on JavaScript to ensure that teaching methods become more efficient, results in evaluation become more equitable, and the ultimate results are improved student performance. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18139731 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Powered Teacher Assistant: Automated Grading and Personalized Feedback Harshal Shriram Patil Shruti Vikas Badgujar Sanika Satish Patil Aakash Anil Dhamane Payal Bhikanrao Patil AI-Powered Teacher Assistant Automated Grading Personalized Learning Feedback Machine Learning Optical Character Recognition (OCR) Natural Language Processing (NLP) Educational Technology. This is an artificial intelligence-based teaching assistant that makes the grading process easier and provides students with specific and personalized feedback. With the use of Optical Character Recognition (OCR) and natural-language processing (NLP), the system is capable of processing both handwritten and electronic submissions, requiring a significantly smaller amount of manual work to carry out the process of assessment. Using deep-learning algorithms (trained on TensorFlow and PyTorch) the assistant derives semantic meaning in response to the students and provides explicit and practical recommendations of what to do better. The solution is hosted on scalable cloud infrastructure, ensuring there is minimal latency and maximum availability even when all classrooms are in use. The system is developed using Python as the backend logic and a user-friendly interface on JavaScript to ensure that teaching methods become more efficient, results in evaluation become more equitable, and the ultimate results are improved student performance. |
| title | AI-Powered Teacher Assistant: Automated Grading and Personalized Feedback |
| topic | AI-Powered Teacher Assistant Automated Grading Personalized Learning Feedback Machine Learning Optical Character Recognition (OCR) Natural Language Processing (NLP) Educational Technology. |
| url | https://doi.org/10.5281/zenodo.18139731 |