Breaking Barriers with Generative Intelligence. Using GI to Improve Human Education and Well-Being : First International Workshop, BBGI 2024, Thessaloniki, Greece, June 10, 2024, Proceedings

Preface
Organization
Contents
Applications, Challenges and Early Assessment of AI and ChatGPT in Education
	1 Introduction
	2 Description of ChatGPT
	3 Applications of ChatGPT
	4 Challenges of ChatGPT
	5 Early Assessment of ChatGPT
	6 Conclusion
	References
Empowering the Metaverse in Education: ChatGPT’s Role in Transforming Learning Experiences
	1 Introduction
	2 Background
		2.1 The Metaverse: A Historical Overview and Its Key Features
		2.2 ChatGPT: Origins, Capabilities, and Applications
	3 The Current Landscape of the Metaverse in Education
		3.1 Usage in Educational Institutions
		3.2 Benefits of Metaverse-Based Learning
		3.3 Limitations and Challenges
	4 ChatGPT’s Role in Metaverse-Based Learning
		4.1 Integration in Metaverse Platforms
		4.2 Case Studies and Successful Deployments
		4.3 Benefits for Learners
	5 Challenges and Limitations of ChatGPT in Metaverse-Based Learning
		5.1 Ethical Considerations
		5.2 Technical Limitations
		5.3 Data Privacy and Security Concerns
	6 Future Trends and Predictions in Metaverse-Based Education with ChatGPT
		6.1 Emerging Trends in Metaverse-Based Education
		6.2 Evolution of ChatGPT’s Role in the Metaverse
		6.3 Evolution Influential Technologies and Tools
	7 Implications for Stakeholders in Metaverse-Based Education with ChatGPT
		7.1 Implications for Educators
		7.2 Implications for Learners
		7.3 Implications for Educational Institutions
		7.4 Implications for Policymakers
	8 Conclusion
	References
Effectiveness of Logistic Regression for Sentiment Analysis of Tweets About the Metaverse
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Data Source
		3.2 Data Preprocessing
		3.3 Model Training
		3.4 Model Evaluation
		3.5 Validation Technique
	4 Results
	5 Discussion of Results
	6 Conclusion
	References
How Students Learn by Validating ChatGPT Responses
	1 Introduction
	2 Background
		2.1 Conversational AI
		2.2 ChatGPT in Education
	3 Method
		3.1 Participants and Research Design
		3.2 Data Collection and Analysis
	4 Results
	5 Discussion
	6 Conclusions and Future Research
	References
Integrating Generative Intelligence into Educational Assessment: A Multi-disciplinary Approach for Enhancing Value-Added Measures in Mass Communication and Management Studies
	1 Introduction
	2 Theoretical Foundations of Generative Intelligence in Education
		2.1 Basics of Generative Intelligence – Definitions and Key Principles
		2.2 Historical Development and Applications of GI in Educational Settings
	3 Value-Added Assessment in Education
		3.1 Explanation of Value-Added Assessment – Definitions and Importance
		3.2 Current Methodologies and Models Used in Value-Added Assessment
	4 Enhancing Educational Assessments in Mass Communication Studies Through Generative Intelligence
	5 Enhancing Educational Assessments in Management Studies Through Generative Intelligence
	6 Conclusion
	References
The Reality of Using Artificial Intelligence to Enhance University Education an Applied Study on a Sample of Media Professors in Arab Universities
	1 Introduction
	2 Research Problem
	3 Research Questions
	4 Study Objectives
	5 Literature Review
		5.1 Research Gap
	6 Methodology
		6.1 Study Design
		6.2 Sample Collection
		6.3 Research Tools
		6.4 Processing and Statistical Analysis
		6.5 Spatial and Temporal Framework
		6.6 Theoretical Aspect
	7 Results
		7.1 The Role of Artificial Intelligence on Faculty Progress
	8 Discussion
	9 Conclusion
	10 Future Work
	References
Comparative Performance of GPT-4, RAG-Augmented GPT-4, and Students in MOOCs
	1 Introduction
	2 Model
	3 Methods
		3.1 Dataset
		3.2 Experiment Design
	4 Results
		4.1 Research Question 1 (RQ1): Does Integrating RAG into the GPT-4 Model Improve the Pedagogical Quality (accuracy And relevance) of Answers in MOOCs?
		4.2 Research Question 2 (RQ2): How Does the Performance of RAG-Augmented GPT-4 Compare to that of Students in MOOC Exercises?
	5 Discussion
	6 Conclusion and Future Work
	References
The Optimisation of Genetic Assessment Test Generation Based on Fuzzy Scoring
	1 Introduction
	2 Literature Review
	3 Model Description
		3.1 Assessment Test Generation
		3.2 Weighted Item Assessment
	4 Results and Discussions
	5 Conclusions
	References
Analyzing the Performance of Distributed Web Systems Within an Educational Assessment Framework
	1 Introduction
	2 Literature Review
	3 Theoretical Foundation of the D-GA-CO Generative Model
		3.1 The GA Component
		3.2 D-CO Component
		3.3 Characteristics of Distributed Web Systems
	4 Results and Discussions
	5 Conclusions
	References
New Paradigm Shift to STEM Education in the United Arab Emirates
	1 Introduction
	2 21St Century Skills
	3 STEM Education and Innovation in UAE
	4 Proposed STEM Education Framework in UAE
	5 Conclusion
	References
Exploring the Role of Generative AI in Medical Microbiology Education: Enhancing Bacterial Identification Skills in Laboratory Students
	1 Introduction
		1.1 Generative Artificial Intelligence (AI) Language Models
		1.2 AI in Medical Microbiology Education
		1.3 Study Objectives
	2 Literature Review
		2.1 Utilization of Technology in Teaching Support
		2.2 The Applications of Generative AI in Education
		2.3 Applications of Generative AI in the Medical Field
		2.4 Using Google Gemini in Educational Settings
		2.5 Ethical Aspects of Generative AI
		2.6 Research Gap
	3 Methodology
		3.1 Examining Different Generative Artificial Intelligence Tools
		3.2 Identification of a Fitting Generative Artificial Intelligence Tool
		3.3 Assessment Creation using Gemini
		3.4 Uploading the Generated Assessment to the Blackboard
		3.5 Pilot Testing
		3.6 Collection of Test Results
	4 Results and Discussion
	5 Conclusion
	6 Future Work
	References
Taxonomy of Intelligent Attendance Systems
	1 Introduction
	2 Taxonomy of Technological Attendance Systems
		2.1 Biometrical Attendance Systems
		2.2 Wireless-Communication-Based Attendance Systems
		2.3 Smartphones Attendance Systems
		2.4 Blockchain Attendance Systems
		2.5 Internet of Things-Based Attendance Systems
		2.6 Management Attendance Systems
	3 Conclusion
	References
Enhancing Education and Well-Being Through Artificial Intelligence: Opportunities and Challenges
	1 Introduction
	2 Aims and Problem Statement
		2.1 Problem Statement
		2.2 Research Objectives
	3 Background
		3.1 Current Trends in AI Within the Educational Sector
		3.2 AI in Education
		3.3 AI in Education: Improving Accessibility
		3.4 Teacher Support and Administrative Efficiency
		3.5 AI in Well-Being: Mental Health Support
	4 Ethical Considerations and Challenges
	5 Case Studies and Practical Applications
	6 Methodology and Data Analysis
	7 Conclusion
	8 Future Outlook
	References
A Transformer-Based Generative AI Model in Education: Fine-Tuning BERT for Domain-Specific in Student Advising
	1 Introduction
	2 Related Work
	3 Bidirectional Encoder Representations from Transformers (BERT) Model
		3.1 BERT Architecture
		3.2 Fine-Tuning process
	4 Experiment (Fine-Tuning)
		4.1 Preparing the Dataset
		4.2 Preprocessing the Data
		4.3 Training the Model
		4.4 Inference Model
	5 Results and Discussion
	6 Conclusion
	References
A Statistical Analysis to Investigate the Factors Affecting Generative AI Use in Education and Its Impacts on Social Sustainability Using SPSS
	1 Introduction
	2 Problem Statement
	3 Research Aim and Objectives
		3.1 Aim
		3.2 Objectives
		3.3 Research Hypotheses
	4 Research Significance and Scope
	5 Literature Review
		5.1 Theoretical Underpinnings: Factors Influencing Technology Adoption in Education
		5.2 Impact of Generative AI on Educational Outcomes
		5.3 Social Sustainability and Education
		5.4 Integration of Generative AI and Social Sustainability
		5.5 Literature Gaps
	6 Methodology
		6.1 Theoretical Framework
		6.2 Data Collection and Analysis:
	7 Discussion
	8 Conclusion
	References
Predicting Student Adaptability to Online Education Using Machine Learning
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Data Source
		3.2 Data Preprocessing
		3.3 Model Selection
		3.4 Evaluation Metrics
	4 Results
		4.1 Model Performance
		4.2 Confusion Matrix
		4.3 ROC Curve
		4.4 Feature Importances
		4.5 Age Distribution Across Adaptability Levels
	5 Discussion
	6 Conclusion
	References
Predicting Student Retention in Higher Education Using Machine Learning
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Data Source
		3.2 Data Preprocessing
		3.3 Model Selection
		3.4 Feature Selection and Engineering
		3.5 Model Training and Evaluation
		3.6 Visualization and Interpretation
	4 Results
		4.1 Confusion Matrix
		4.2 ROC Curve
		4.3 Training and Testing Loss/Accuracy Curves
		4.4 Classification Report
	5 Conclusion
	References
Building and Evaluating a Chatbot Using a University FAQs Dataset
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Data Source
		3.2 Sample Data
		3.3 Data Preprocessing
		3.4 Model Building
		3.5 Model Architecture
	4 Results
		4.1 Evaluation Metrics
		4.2 Graphical Analysis
	5 Conclusion
	References
Comparative Analysis of Classical Machine Learning Techniques for Predicting Students’ Exam Performance
	1 Introduction
	2 Related Work
	3 Methodology
		3.1 Data Preprocessing
		3.2 Feature Selection
		3.3 Model Training
		3.4 Model Evaluation
	4 Results
		4.1 Performance Metrics
		4.2 ROC Curve
		4.3 Detailed Analysis
	5 Conclusion
	References
Exploring Machine Learning’s Role in Education: A Comprehensive Review and Academic Implications
	1 Introduction
	2 Background of the Study
	3 Research Methodology
	4  Results
		4.1 RQ1: Frequently Used Machine Learning Techniques and Algorithms in Educa.
		4.2 RQ2: Significant Predictive Features in ML Models
		4.3 RQ3: Types of Educational Data Utilized in Research
		4.4 RQ4: Educational Outcomes and Improvements Identified with ML Solutions.
	5 Discussion
	6  Conclusion Limitations and future work
	References
Author Index