The presentation argues that artificial intelligence can transform educational assessment by automating grading, generating personalised feedback, detecting plagiarism, and supporting data-driven progress tracking. Its central claim is that AI assessment tools can substantially improve efficiency, consistency, and responsiveness when implemented through careful institutional planning. Automated grading systems use natural language processing, machine learning, and essay-scoring models to evaluate student work against rubrics, while feedback tools analyse response patterns and provide specific, adaptive suggestions for improvement. The slides emphasise practical benefits such as major reductions in grading time, instant results, analytics, and scalable individualisation. A central case is plagiarism and AI-content detection: tools such as Turnitin, GPTZero, Winston AI, and Copyleaks can support academic integrity, but the presentation warns that detection systems may produce false positives, show variable accuracy, and disproportionately affect non-native speakers. For this reason, AI detection should be treated as supporting evidence rather than a sole basis for academic decisions. The implementation roadmap recommends needs assessment, tool selection, pilot testing, staff training, LMS integration, gradual rollout, monitoring, and optimisation. The conclusion is that AI can enhance assessment and feedback only when embedded within transparent policy, stakeholder engagement, ethical oversight, continuous training, and measurable success indicators rather than adopted as a purely technical shortcut.
