<![CDATA[Traditional teaching practices are often characterized by passive learning, limited interactivity, and a lack of real-time contextual feedback, which struggle to meet the evolving expectations of 21st-century learners. These limitations hinder learner engagement, knowledge retention, and the development of critical thinking skills. In response to these shortcomings, educators have increasingly explored innovative technologies. Among them, Spatial Computing stands out for its ability to merge physical and digital environments, enabling immersive, hands-on learning that traditional tools like video lectures or slide-based content cannot provide. This systematic literature review analyses 16 peer-reviewed studies published between 2020 and 2025, selected from Google Scholar, IEEE Xplore, ScienceDirect, and Springer. It investigates Spatial Computing's educational applications, benefits, challenges, and the technologies supporting its use. The findings reveal that Spatial Computing bridges virtual and reality, thus making learning content multidimensional. This leads to higher retention, active learning, and critical thinking. The findings report on both the great challenge and opportunity of making Spatial Computing available for learning environments. On one hand, it enables interactive simulation learning environments, real-time visualizations of information, and in-the-sim empirical manipulation of objects. On the other hand, it is limited by challenges associated with prohibitively expensive development costs, technical sophistication, and calls for comprehensive evaluation methodologies, inhibiting wide uptake.Additionally, this research highlights the necessity of close interdisciplinarity and the application of sound design methodologies to effectively leverage Spatial Computing. Overall, the review substantiates that Spatial Computing has the promise of radically overhauling conventional education through interactive, immersive, and personalized learning experiences. Future research needs should focus on simplifying the complexities of technology implementation, optimizing the system's design, and developing benchmarked standards for evaluating the learning effects of Spatial Computing.]]>
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