A Systematic Review of AI-Powered Assessment and Feedback to Enhance Teaching Effectiveness in Higher Education

Abstract

The growing use of artificial intelligence in higher education assessment offers a real chance to improve teaching effectiveness. However, the practical, ethical, and pedagogical aspects of this shift are not yet well understood. This paper presents a systematic review of 127 studies selected from 699 candidate papers published between 2022 and 2025. The review examines evidence across five themes: feedback personalisation, assessment accuracy, ethical and equity concerns, human-in-the-loop integration, and pedagogical impact. The findings show that AI tools, particularly GPT-4, can deliver personalised, timely, and scalable feedback that improves student engagement and academic outcomes while reducing educator workload. However, AI-generated feedback is often less sensitive to context, empathy, and higher-order thinking tasks, especially in creative and humanities subjects. Hybrid models that combine AI with human oversight are the well-supported approach, as they improve grading accuracy, fairness, and student trust. Issues such as algorithmic bias, data privacy, lack of transparency, and weak governance remain key ethical challenges that need strong institutional responses. This review offers a clear evidence base to guide educators, policymakers, and technologists on how to use AI-enhanced assessment in a responsible and sustainable way.