Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation
The present study underscores the critical role of state-of-the-art machine learning and deep learning technologies in reshaping the traditional educational system, particularly in the context of digital examinations. Nevertheless, this transformation introduces significant challenges that require a...
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| Format: | Article |
| Language: | English |
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Bilijipub publisher
2023-12-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
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| Online Access: | https://jaism.bilijipub.com/article_186540_57e12d847e51dfc1ee99e290e8d72aa2.pdf |
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| _version_ | 1849762732408045568 |
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| author | Shilpi Gautam Asadi Srinivasulu |
| author_facet | Shilpi Gautam Asadi Srinivasulu |
| author_sort | Shilpi Gautam |
| collection | DOAJ |
| description | The present study underscores the critical role of state-of-the-art machine learning and deep learning technologies in reshaping the traditional educational system, particularly in the context of digital examinations. Nevertheless, this transformation introduces significant challenges that require attention to ensure its success. One prominent challenge pertains to the development of ethical and impartial assessment algorithms. With the integration of machine learning and deep learning methods into digital examinations, concerns related to assessment bias and fairness have surfaced, necessitating research to design algorithms capable of delivering unbiased evaluations for students from diverse backgrounds. Additionally, there is a pressing need to delve into the ethical implications of using artificial intelligence in educational assessment. Furthermore, substantial concerns revolve around data security and privacy. The digital examination process entails the collection and secure storage of sensitive student data, raising worries about potential data security breaches and violations of privacy. To mitigate these risks, the proposed system aims to implement robust fairness-aware assessment algorithms while also incorporating advanced encryption and privacy-preserving techniques. This comprehensive approach is geared toward safeguarding student data, preventing academic dishonesty and data breaches, and ensuring compliance with data protection regulations, all with the aim of providing equitable assessments and maintaining data privacy in the context of digital examinations enhanced by machine learning and deep learning technologies. |
| format | Article |
| id | doaj-art-e1abe247dcb3478ba0326f48038317ba |
| institution | DOAJ |
| issn | 3041-850X |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Journal of Artificial Intelligence and System Modelling |
| spelling | doaj-art-e1abe247dcb3478ba0326f48038317ba2025-08-20T03:05:39ZengBilijipub publisherJournal of Artificial Intelligence and System Modelling3041-850X2023-12-010101516010.22034/jaism.2023.426275.1011186540Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination TransformationShilpi Gautam0Asadi Srinivasulu1Department of Education, University of Chaudhary Charan Singh, Meerut, Uttar Pradesh, 250001, IndiaIndian Institute of Information Technology, Allahabad, Uttar Pradesh, 211012, IndiaThe present study underscores the critical role of state-of-the-art machine learning and deep learning technologies in reshaping the traditional educational system, particularly in the context of digital examinations. Nevertheless, this transformation introduces significant challenges that require attention to ensure its success. One prominent challenge pertains to the development of ethical and impartial assessment algorithms. With the integration of machine learning and deep learning methods into digital examinations, concerns related to assessment bias and fairness have surfaced, necessitating research to design algorithms capable of delivering unbiased evaluations for students from diverse backgrounds. Additionally, there is a pressing need to delve into the ethical implications of using artificial intelligence in educational assessment. Furthermore, substantial concerns revolve around data security and privacy. The digital examination process entails the collection and secure storage of sensitive student data, raising worries about potential data security breaches and violations of privacy. To mitigate these risks, the proposed system aims to implement robust fairness-aware assessment algorithms while also incorporating advanced encryption and privacy-preserving techniques. This comprehensive approach is geared toward safeguarding student data, preventing academic dishonesty and data breaches, and ensuring compliance with data protection regulations, all with the aim of providing equitable assessments and maintaining data privacy in the context of digital examinations enhanced by machine learning and deep learning technologies.https://jaism.bilijipub.com/article_186540_57e12d847e51dfc1ee99e290e8d72aa2.pdfdata securityeducation transformationmachine learningdeep learningdigital examinationsethical assessment |
| spellingShingle | Shilpi Gautam Asadi Srinivasulu Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation Journal of Artificial Intelligence and System Modelling data security education transformation machine learning deep learning digital examinations ethical assessment |
| title | Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation |
| title_full | Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation |
| title_fullStr | Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation |
| title_full_unstemmed | Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation |
| title_short | Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation |
| title_sort | revolutionizing education harnessing machine learning and deep learning for digital examination transformation |
| topic | data security education transformation machine learning deep learning digital examinations ethical assessment |
| url | https://jaism.bilijipub.com/article_186540_57e12d847e51dfc1ee99e290e8d72aa2.pdf |
| work_keys_str_mv | AT shilpigautam revolutionizingeducationharnessingmachinelearninganddeeplearningfordigitalexaminationtransformation AT asadisrinivasulu revolutionizingeducationharnessingmachinelearninganddeeplearningfordigitalexaminationtransformation |