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

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