Transforming Student Evaluation with Adaptive Intelligence and Performance Analytics
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909528611618816 |
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| author | S, Pushpalatha K Mangalur, Abhishek Hegde, Ketan Badachi, Chetan Aamir, Mohammad |
| author_facet | S, Pushpalatha K Mangalur, Abhishek Hegde, Ketan Badachi, Chetan Aamir, Mohammad |
| contents | The development in Artificial Intelligence (AI) offers transformative potential for redefining student assessment methodologies. This paper aims to establish the idea of the advancement of Artificial Intelligence (AI) and its prospect in reshaping approaches to assessing students. It creates a system for the evaluation of students performance using Artificial intelligence, and particularly the Gemini API for the generation of questions, grading and report on the students performances. This is to facilitate easy use of the tools in creating, scheduling, and delivering assessments with minimal chances of cheating through options such as full screen and time limit. There are formats of questions in the system which comprises multiple choice, short answers and descriptive questions, developed by Gemini. The most conspicuous feature is the self-checking system whereby the user gets instant feedback for the correct score that each of the students would have scored instantly with explanations about wrong answers. Moreover, the platform has intelligent learning progressions where the user will be able to monitor his/her performances to be recommended a certain level of performance. It will allow students as well as educators to have real-time analytics and feedback on what they are good at and where they need to improve. Not only does it make the assessment easier, but it also improves the levels of accuracy in grading and effectively strengthens a data based learning process for students. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transforming Student Evaluation with Adaptive Intelligence and Performance Analytics S, Pushpalatha K Mangalur, Abhishek Hegde, Ketan Badachi, Chetan Aamir, Mohammad Computers and Society Artificial Intelligence The development in Artificial Intelligence (AI) offers transformative potential for redefining student assessment methodologies. This paper aims to establish the idea of the advancement of Artificial Intelligence (AI) and its prospect in reshaping approaches to assessing students. It creates a system for the evaluation of students performance using Artificial intelligence, and particularly the Gemini API for the generation of questions, grading and report on the students performances. This is to facilitate easy use of the tools in creating, scheduling, and delivering assessments with minimal chances of cheating through options such as full screen and time limit. There are formats of questions in the system which comprises multiple choice, short answers and descriptive questions, developed by Gemini. The most conspicuous feature is the self-checking system whereby the user gets instant feedback for the correct score that each of the students would have scored instantly with explanations about wrong answers. Moreover, the platform has intelligent learning progressions where the user will be able to monitor his/her performances to be recommended a certain level of performance. It will allow students as well as educators to have real-time analytics and feedback on what they are good at and where they need to improve. Not only does it make the assessment easier, but it also improves the levels of accuracy in grading and effectively strengthens a data based learning process for students. |
| title | Transforming Student Evaluation with Adaptive Intelligence and Performance Analytics |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2503.04752 |