Review of the Literature on AI-Enhanced Augmented Reality in Education

This chapter provides a comprehensive review of the literature regarding AI-enhanced Augmented Reality (AR). It serves as the foundational knowledge base for the study, offering insights into relevant theories, concepts, and prior research studies. The chapter begins with an “Overview” section, outlining the purpose and significance of the literature review in establishing a robust theoretical framework. It emphasizes the necessity of exploring spatial ability, AR technology, and learning theories to comprehend their interconnections and implications for the development of a mobile training system. The subsequent section, “Spatial Ability: Review of Theories,” delves into the concept of spatial ability, particularly within engineering disciplines. Various theories and models that elucidate spatial ability, its components, and its relevance in the context of success in engineering are discussed in detail, providing a theoretical underpinning. The “Augmented Reality in Education” section explores the use of AR in educational settings, with a focus on engineering education. It discusses the advantages and potential of AR technology in enhancing spatial ability and facilitating learning, considering both pedagogical and technical aspects. The “Learning Theories” section introduces different learning theories, notably Bloom’s taxonomy and the Structure of Observed Learning Outcomes (SOLO) taxonomy. It outlines these taxonomies’ principles, stages, and hierarchical levels, emphasizing their relevance to instructional design and assessing learning outcomes. The “Literature Review” section elucidates the methodology employed in conducting the literature review, including the evaluation paper screening process. Findings from the review are presented, addressing various research questions, such as the benefits and drawbacks of AR in spatial ability training, adaptive features in AR applications, evaluation methods, and the specific aspects of spatial abilities assessed using AR.