| Summary: | Abstract Traditional grading systems in higher education face significant challenges, including time inefficiency, subjective bias, and scalability issues, necessitating innovative solutions. This narrative review synthesises literature from 2018 to 2025, examining AI-powered grading and feedback systems, analysing 77 core studies across multiple databases. AI technologies, particularly machine learning, natural language processing, and computer vision, demonstrate significant potential in automating assessment processes, providing consistent grading, and delivering personalised feedback. Benefits include reduced educator workload, faster turnaround times, and enhanced learning experiences. However, critical challenges persist, including algorithmic bias, data privacy concerns, lack of transparency, and the need for human oversight. While AI-driven assessment tools offer transformative potential for higher education, successful implementation requires careful integration with human expertise, robust ethical frameworks, and continuous validation to ensure equitable and effective educational outcomes.
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