## 1 Introduction
### Network Visualization of the Literature on Augmented and Mixed Reality in Education
Some terms, like \"immersive virtual reality\" and \"safety training\", are connected but positioned on the periphery, possibly indicating specialized or niche areas within the broader context. The term \"students\" is connected to \"curricula\", \"learning systems\", and \"computer-aided instruction\", highlighting a focus on educational content. One limitation of the scientific literature that compares mixed reality (MR) to traditional PowerPoint methods to assess their influence is the scarcity of attention given to the age factor, with the result that it is infrequently acknowledged as a significant variable. In our review of the existing research, we could not identify any studies that include age as a parameter in their assessment of the efficacy of these instructional approaches. One factor that may affect the adoption, and effectiveness, of technology enhanced learning (TEL) in higher education is the age of the students, which may influence their use of, and attitudes towards, technology, as well as their learning outcomes and cognitive load. However, studies of the relationship between TEL and age are scarce and inconclusive, and the ones that do exist use different definitions, measures, and methods. Some studies have found that, while mature students (usually defined as those over 21 years-of-age) use fewer technologies less frequently than younger students, they tend to have similar, or even slightly more positive, attitudes towards technology [38; 39]. Other studies found that age is not a significant predictor of technology use or attitudes, and identified other variables, such as computer experience, self-efficacy, and perceived usefulness, to be more important [40]. Moreover, some studies have suggested that age may interact with other factors, such as the type and quality of the TEL intervention, the domain and level of the course, the assessment method, and prior knowledge and experience of students, to influence students' learning outcomes and cognitive load. For example, Ref. [41] found that younger students performed better and reported lower cognitive load than older students when using a smart classroom system that supported a novel learning flow; and that students performed better and reported lower cognitive load when using a conventional classroom system. These findings indicate that age is a complex and multifaceted variable that needs to be considered in the design, implementation, and evaluation of TEL interventions in higher education. Oversight of this is a critical gap, given the diverse age groups present in higher educational settings. In recognizing this, we incorporate age as a key variable in the investigation, and suggest that it may play an important role in determining the effectiveness of instructional methods, particularly in the context of immersive technologies like MR. Learners across different age brackets possess distinct cognitive abilities, preferences, and learning styles, and it is essential to understand how age interacts with the choice of instructional methods when designing tailored and effective educational interventions. By acknowledging and addressing this gap, our research does not not only contribute to the existing knowledge about the comparative effectiveness of MR and PowerPoint but also argues that the age-related nuances of these methodologies should be explored in more depth and detail. The following sections present our method, findings, and analysis, and explore the complex interplay of age, instruction methods, and learning outcomes. Through this approach, we aim to provide an overview of the potential benefits and drawbacks of using immersive technologies in various educational contexts.