Revolutionizing Education: Harnessing Machine Learning and Deep Learning for Digital Examination Transformation

Abstract

The present study underscores the critical role of state-of-the-art machine learning (ML) and deep learning (DL) 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 ML and DL 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 ML and DL technologies.