Extended Reality in Higher Education: A Systematic Review on Simulation‐Based Learning in Algorithm Education

Abstract

Algorithm education is a core component in computer science, but it remains difficult to teach effectively due to abstract content and disengaging traditional instructional methods. Many students struggle with algorithm concepts, leading to low motivation, high dropout rates, and insufficient preparation for real‐world computing careers. This systematic review of 27 studies between 2011 and 2025 followed PRISMA guidelines, analyzing simulation‐based learning (SBL) in algorithm education using eXtended reality (XR), and taking into account their pedagogical focus, technology, learning approach, features, and evaluation attributes and methodologies. Findings show that virtual reality (VR) is the most commonly used XR tool for teaching algorithms, particularly sorting and searching, with a consistently positive impact on engagement and motivation. However, the evidence for improved understanding of concepts was weaker as most of the studies used small sample sizes, short interventions, and novelty effects. The literature focused on making algorithms visible and interactive, yet visibility alone does not guarantee the abstraction and logical reasoning that algorithm thinking requires, and it remains an open question whether XR is suited to these complex processes. XR‐based SBL shows substantial promise for algorithm education, but future research must prioritize deeper algorithm thinking, accessible platforms, and rigorous longitudinal evaluation to support sustainable adoption in higher education, thereby better preparing students for industry demands.