How does leveraging artificial intelligence in assessments impact student outcomes? a systematic review

Abstract

Advancements in Artificial Intelligence (AI) are having a profound impact across numerous domains, including education, particularly in the area of assessment. Within higher education, AI-based assessment has gained increasing attention for its potential to enhance student learning processes and outcomes. Following PRISMA guidelines and covering research published between 1997 and 2024, this systematic literature review (SLR) analyzes 159 studies that apply AI techniques, including machine learning (ML), deep learning (DL), and large language models (LLMs), in formative and summative assessment contexts to predict student outcomes. The findings indicate that, while AI integration can enhance assessment strategies and learning outcomes, classification-based models dominate the literature, and more than 80% of studies rely on private or institution-specific datasets, limiting reproducibility and large-scale validation. This review offers a comprehensive comparative synthesis of AI-driven formative and summative assessment approaches in higher education, highlighting methodological trends, evidence, and research gaps.