Special Topics in Artificial Intelligence and Augmented Reality : The Case of Spatial Intelligence Enhancement

• Record Nr.: UNINA9910806191703321
• Autore: Papakostas Christos
• Pubbl/distr/stampa: Cham : , : Springer International Publishing AG, , 2024; ©2024
• ISBN: 3-031-52005-X
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (183 pages)
• Collana: Cognitive Technologies Series
• Altri autori (Persone): TroussasChristos; SgouropoulouCleo
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Foreword -- Preface -- Contents -- Chapter 1: Introduction and Overview of AI-Enhanced Augmented Reality in Education -- 1.1 Overview -- 1.2 Motivation -- 1.3 Research Questions -- 1.4 Approach and Structure -- References -- Chapter 2: Review of the Literature on AI-Enhanced Augmented Reality in Education -- 2.1 Overview -- 2.2 Spatial Ability: Review of Theories -- 2.2.1 Spatial Ability in Engineering -- 2.3 Augmented Reality in Education -- 2.3.1 AR in Engineering Education -- 2.4 Learning Theories -- 2.4.1 The Bloom's Taxonomy -- 2.4.2 The SOLO Taxonomy -- 2.4.3 Comparison of the Learning Theories -- 2.5 Literature Review -- 2.5.1 Planning the Review (Review Protocol) -- 2.5.2 Conducting the Review -- 2.5.3 Screening of the Evaluation Papers -- 2.5.4 Advantages of AR in Spatial Ability Training (RQ1) -- 2.5.4.1 Learner Outcomes -- 2.5.4.2 Pedagogical Affordances -- 2.5.4.3 Technical Perspectives -- 2.5.5 Limitations of AR in Spatial Ability Training (RQ2) -- 2.5.6 Exploration of the Incorporation of Adaptivity and Personalization in AR Applications (RQ3) -- 2.5.7 Aspects of Spatial Abilities Having Been Evaluated Using AR (RQ4) -- 2.5.8 Evaluation Methods Considered for AR Applications in Educational Scenarios (RQ5) -- 2.6 Summary -- References -- Chapter 3: AI-Driven and SOLO-Based Domain Knowledge Modeling in PARSAT AR Software -- 3.1 Overview -- 3.2 Domain Model -- 3.2.1 Objectives -- 3.3 Domain Knowledge Alongside ; SOLO Taxonomy -- 3.4 Examples of Learning Activities of Each SOLO Level -- 3.5 Summary -- References -- Chapter 4: Fuzzy Logic for Modeling the Knowledge of Users in PARSAT AR Software -- 4.1 Overview -- 4.2 Fuzzy Logic Algorithm -- 4.3 Initialization Process -- 4.4 Fuzzy Sets -- 4.5 Fuzzy Rule Base -- 4.6 Mamdani's Inference System -- 4.7 Defuzzification.; 4.8 Adaptation of the Learning Activities Based on Fuzzy Weights -- 4.8.1 Decision Making -- 4.9 Summary -- References -- Chapter 5: Artificial Intelligence-Enhanced PARSAT AR Software: Architecture and Implementation -- 5.1 Overview -- 5.2 System Architecture -- 5.2.1 Hardware Layer -- 5.2.1.1 Tracking -- 5.2.1.2 Processing -- 5.2.1.3 Interacting -- 5.2.2 Software Layer -- 5.2.2.1 User Interface -- 5.2.2.2 3D Rendering Engine -- 5.2.3 Data Layer -- 5.2.3.1 Marker Database -- 5.2.3.2 3D Models Database -- 5.2.3.3 Interaction Model -- 5.3 Implementation of the System -- 5.3.1 User Interface of PARSAT -- 5.3.2 Fuzzy Logic Controller Implementation with C# Scripting -- 5.3.2.1 System Initialization -- 5.3.2.2 Linguistic Variables and Membership Functions -- 5.3.2.3 Fuzzification Process Implementation -- 5.3.2.4 Rules of the System -- 5.3.2.5 Evaluation of the Rules -- 5.3.2.6 Defuzzification -- 5.4 Summary -- References -- Chapter 6: Multi-model Evaluation of the Artificial Intelligence-Enhanced PARSAT AR Software -- 6.1 Overview -- 6.2 Evaluation Framework -- 6.2.1 Research Sample -- 6.2.2 Training Preparation -- 6.3 t-Test Analysis of Students' Feedback -- 6.4 Comparative Analysis of Pre-test/Post-test Model in Achieving the Learning Outcomes -- 6.4.1 Discussion of the Results -- 6.5 Extended Technology Acceptance Model for Detecting Influencing Factors -- 6.5.1 Existing Acceptance Models -- 6.5.2 Proposed Extended Model -- 6.5.3 Research Model and Hypotheses -- 6.5.4 Research Instruments -- 6.5.5 Data Analysis -- 6.5.6 Model Validation -- 6.5.6.1 Measurement Model -- 6.5.6.2 Structural Model -- 6.6 Summary -- References -- Chapter 7: Conclusions of AI-Driven AR in Education -- 7.1 Overview -- 7.2 Conclusions and Discussion -- 7.3 Contribution to Intelligent Tutoring Systems -- 7.4 Contribution to Domain Knowledge Model.; 7.5 Contribution to Student Modeling -- 7.6 Contribution to Electronic Assessment -- 7.7 Future Work -- References.