Rethinking Higher Education Teaching and Assessment In-Line with AI Innovations: A Systematic Review and Meta-Analysis

With the rapid advancement of artificial intelligence (AI) technologies, higher education institutions are increasingly exploring innovative ways to rethink teaching and assessment practices. This research paper examines the implications of AI on assessments in online learning environments. Specifically, the objectives of this study were to evaluate the effectiveness of AI-powered teaching methodologies in enhancing student engagement and learning outcomes in online education settings and, secondly, to analyze the impact of AI-driven assessment tools on the accuracy, reliability, and fairness of evaluating student performance in online learning environments through a systematic review and meta-analysis of existing literature. The study adopted activity theory to understand the issues around AI and assessment. The study adopted a mixed-methods design. The study adopted the use of meta-analysis in order to statistically combine results from multiple studies on a particular topic to provide a more comprehensive and reliable summary of the overall findings. The study found that to guarantee moral and just practices, there are issues with the integration of AI in online learning that need to be resolved. Key issues included data privacy, algorithmic prejudice, and the role of human instructors in the administration of the assessments online, carefully considered and addressed in a proactive manner. These findings provided insights on how AI can transform traditional teaching methods and assessment strategies, creating an AI-crowded environment that fosters student learning and academic success. Based on the findings, the study recommends that there is a need to integrate pedagogical strategies that leverage AI innovation, such as adaptive learning approaches, real-time feedback mechanisms, or interactive simulations, to improve teaching effectiveness and student performance in online settings.