<![CDATA[This study examines the transformative impact of Artificial Intelligence (AI) on Computer-Based Testing (CBT) through a systematic literature review (SLR) following the PRISMA 2020 protocol. The research identifies key opportunities, including automated grading (reducing instructor workload by 70%) and adaptive testing (enhancing personalized assessments), alongside critical challenges such as algorithmic bias (particularly in speech recognition systems) and privacy concerns in AI-based proctoring. Analysis of 95 peer-reviewed studies (2015-2024) reveals a significant post-2020 surge in research, driven by digital education demands during the pandemic, with current trends focusing on Generative AI integration (25% of studies) and bias mitigation (35%). The findings highlight the need for ethical and equitable development of AI-enhanced CBT systems that prioritize both technological innovation and ethical considerations, particularly regarding fairness, transparency, and data protection. The study concludes with recommendations for future research directions, including the development of Explainable AI (XAI) frameworks and inclusive assessment models. These insights provide valuable guidance for educators, policymakers, and technology developers working to optimize AI applications in educational assessment.]]>
Copyrights © 2025
