Special Topics in Artificial Intelligence and Augmented Reality

The Case of Spatial Intelligence Enhancement

verfasst von: Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Verlag: Springer Nature Switzerland

Buchreihe : Cognitive Technologies

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This monograph explores the synergy of Artificial Intelligence (AI), Augmented Reality (AR) and cognitive processes to enhance spatial abilities — an integral aspect of cognitive development. The ability to comprehend and manipulate spatial information is not only fundamental to our understanding of the physical world but also plays a pivotal role in numerous academic and professional fields. Recognizing the profound impact of spatial ability on scientific disciplines and educational achievement, this monograph takes on the challenge of enhancing spatial skills among users.

The authors present the design and development of a mobile training system that incorporates AR features to enhance learners’ spatial ability. Involving mental generation, transformation, and rotation of visual images for understanding spatial relationships, spatial ability is closely linked to success in various scientific disciplines and educational pursuits. While spatial visualization skills tests are available, this monograph takes a unique approach by focusing on developing targeted interventions to improve spatial ability. It aims to unlock new avenues for cognitive growth and dive into the untapped potential within the realm of spatial intelligence.

Beyond its emphasis on spatial ability enhancement, this monograph goes above and beyond traditional approaches by integrating AI techniques into the training system. As such, it aims to provide personalized and adaptive learning experiences for learners. The training system, through intelligent techniques, dynamically analyzes individual learners’ strengths, weaknesses, and progress, tailoring content and challenges to their specific needs. This effort establishes a new frontier in educational technology, offering a groundbreaking solution that not only augments spatial ability development but also showcases the transformative potential of AI in reshaping the learning experience.

The book is a valuable resource for researchers, educators, developers and technology enthusiasts, as it exemplifies the profound impact of AI and AR in shaping the future of online learning experiences.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction and Overview of AI-Enhanced Augmented Reality in Education

Abstract

This chapter of this book serves as an introductory chapter, offering readers a comprehensive overview of the research. It begins with an “Overview” section that outlines the main sections to provide readers with a roadmap of what to expect in the subsequent sections. The “Motivation” section explores the reasons behind conducting this research, emphasizing the significance of spatial ability in human intelligence and its connection to success in scientific and educational fields. It also discusses the potential benefits of augmented reality in enhancing spatial ability and the importance of adaptivity in training systems, which serves as a motivation for the study. In the “Research Questions” section, specific research questions are introduced, designed to address gaps in existing literature and examine the impact of a proposed blended mobile system on fostering spatial ability. These questions provide a clear focus for the study and guide the subsequent chapters. It highlights the iterative nature of the research and presents the overall structure of the book, helping readers understand how the subsequent chapters build upon each other.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Chapter 2. Review of the Literature on AI-Enhanced Augmented Reality in Education

Abstract

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.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Chapter 3. AI-Driven and SOLO-Based Domain Knowledge Modeling in PARSAT AR Software

Abstract

This chapter of this book centers on the enrichment of the domain knowledge model through the incorporation of the Structure of Observed Learning Outcomes (SOLO) taxonomy. It investigates the correlation between the domain knowledge model and the SOLO taxonomy, offering practical instances of learning tasks aligned with each SOLO level. The “Overview” section introduces the chapter’s purpose, emphasizing the significance of aligning learning activities with SOLO-defined cognitive levels. The “Domain Model” section outlines the model’s objectives and relevance in spatial ability training, highlighting specific knowledge areas targeted in the mobile training system. In the “Domain Knowledge alongside SOLO Taxonomy” section, the integration of the SOLO taxonomy into the domain model is explored. This section underscores the importance of gradually developing students’ spatial ability through scaffolded learning experiences. The “Examples of Learning Activities of Each SOLO Level” section furnishes detailed examples of learning activities spanning from prestructural to extended abstract SOLO levels. These examples illustrate the practical application of the SOLO taxonomy within the domain knowledge model. The “Summary” section concludes by summarizing key points, highlighting the integration of the SOLO taxonomy as a scaffolding mechanism to enhance spatial ability training. This chapter serves as the foundation for subsequent chapters, which delve into the implementation and evaluation of the mobile training system.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Chapter 4. Fuzzy Logic for Modeling the Knowledge of Users in PARSAT AR Software

Abstract

This chapter of the book centers on the application of fuzzy logic for modeling students’ knowledge, with a primary focus on enhancing spatial ability training through personalized and adaptive learning experiences. The chapter begins with an overview, emphasizing the utility of fuzzy logic in capturing and adapting to students’ knowledge levels. It underscores the significance of tailoring learning activities to individual students’ needs. The core components of the fuzzy logic algorithm are elaborated upon in detail. This includes an explanation of how fuzzy logic handles imprecise and uncertain knowledge through linguistic variables and fuzzy sets. The initialization process is discussed, outlining how the model is set up to capture students’ knowledge levels at the outset of training, underscoring the importance of accurate initialization for effective adaptation of learning activities. The concept of fuzzy sets and their role in representing linguistic variables is explored, shedding light on how they measure the degree of membership or fuzzy truth values associated with various knowledge levels. The construction of the fuzzy rule base is explained, detailing how rules are defined to link linguistic variables and their corresponding fuzzy sets to appropriate learning activities, emphasizing the rule-based decision-making nature of fuzzy logic. Mamdani’s inference system, a crucial component of the fuzzy logic model, is examined in terms of how it combines fuzzy rules to determine adaptive learning activities based on students’ knowledge levels. The process of defuzzification is described, highlighting its role in converting fuzzy outputs into actionable decisions. The chapter concludes by illustrating how fuzzy weights obtained through the fuzzy logic model are employed for real-time adaptation of learning activities, influencing the selection and customization of learning materials.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Chapter 5. Artificial Intelligence-Enhanced PARSAT AR Software: Architecture and Implementation

Abstract

This chapter offers a comprehensive exploration of the architecture and practical implementation of a mobile training system, enriched with augmented reality (AR) features and adaptive capabilities based on fuzzy logic. It commences with an overview that encapsulates the core concepts and objectives of the system, followed by a detailed exposition of its structural underpinnings. The “Overview” section provides a high-level synopsis of the mobile training system’s architecture, with a specific emphasis on its integration of AR features and its pivotal role in enhancing spatial ability training. The subsequent section, “System Architecture,” conducts an intricate examination of the system’s architecture, delineating the distinct layers involved, namely the hardware layer, software layer, and data layer. This section elucidates the interplay of components within each layer, fostering a comprehensive understanding of the system’s holistic structure. The “Hardware Layer” section delves into the physical components of the system, elucidating their roles in tracking user movements, computational processes, and facilitating user interactions in real-time. In “Software Layer,” the focus shifts to the software components, encompassing the user interface’s interactive capabilities and the 3D rendering engine’s pivotal role in creating and presenting virtual elements within the real-world context. The “Data Layer” section addresses data storage and management, encompassing marker databases for AR tracking, 3D models repositories, and interaction models defining system rules and behaviors. The chapter further illuminates the practical implementation of the system, specifically detailing the user interface’s design and its interaction with AR learning activities. Additionally, it elucidates the incorporation of a fuzzy logic controller through C# scripting, facilitating adaptive learning based on fuzzy weight parameters.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Chapter 6. Multi-model Evaluation of the Artificial Intelligence-Enhanced PARSAT AR Software

Abstract

This chapter of the book delves into the meticulous evaluation of an Artificial Intelligence-enhanced Augmented Reality (AR) mobile training system designed to enhance spatial ability training. This chapter adopts a multi-model evaluation approach, employing various research methods and techniques to comprehensively assess the system’s effectiveness and impact. The chapter commences with an overview stressing the importance of evaluating the system’s impact on spatial ability training and introduces the need for a comprehensive evaluation framework. It then delves into the “Evaluation Framework,” outlining the overall structure, research sample, and participant preparation for the training phase. The “t-Test Analysis of Students’ Feedback” section focuses on analyzing participant feedback, utilizing t-test analysis to identify differences in feedback between the experimental and control groups. This sheds light on participants’ perceptions and satisfaction with the system. Next, the “Comparative Analysis of Pre-Test/Post-Test Model” assesses the system’s impact on spatial ability development through a pre-test and post-test model, providing valuable insights into its effectiveness. The chapter also introduces an “Extended Technology Acceptance Model” tailored to evaluate the human-system interaction, exploring factors influencing system acceptance and usability.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Chapter 7. Conclusions of AI-Driven AR in Education

Abstract

This book concluding chapter encapsulates the essence of our extensive research, which explores the intricate intersection of artificial intelligence (AI) and augmented reality (AR). Within these pages, we synthesize the pivotal findings, implications, and contributions distilled from our comprehensive study, shedding light on the multifaceted relationship between these transformative technologies. This final segment of our scholarly endeavor begins by distilling the salient findings unearthed throughout our multidimensional inquiry. Our investigation spans diverse domains, including knowledge representation, automated reasoning, multiagent systems, hybrid cognitive technologies, human-centered design, socio-technical systems, human-computer interaction, intelligent decision support systems, and prediction systems. Within these areas, we uncover profound insights that illuminate the vast potential of integrating AI into the realm of AR. Beyond elucidating these insights, our work extends to far-reaching implications that resonate across various fields. We underscore the transformative influence of AI-AR synergy and offer a roadmap for innovators, researchers, and practitioners keen on harnessing these technologies’ potential. By bridging existing knowledge gaps, we lay the foundation for a future characterized by collaborative human-machine partnerships. In summary, this book concluding chapter represents the culmination of our collective scholarly endeavors. It offers a panoramic view of the intricate interplay between AI and AR, showcasing the boundless possibilities that await those who dare to traverse this dynamic landscape. By marrying human creativity with technological innovation, we aim to shape the course of future developments in this field.

Christos Papakostas, Christos Troussas, Cleo Sgouropoulou

Titel: Special Topics in Artificial Intelligence and Augmented Reality
verfasst von: Christos Papakostas
Christos Troussas
Cleo Sgouropoulou
Verlag: Springer Nature Switzerland
Electronic ISBN: 978-3-031-52005-1
Print ISBN: 978-3-031-52004-4
DOI: https://doi.org/10.1007/978-3-031-52005-1