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Introduction

Artificial Intelligence (AI) has become increasingly popular in various fields, including education, where automated essay grading systems are being used to provide feedback to students. However, these systems are often considered ‘black boxes,’ as they provide scores without explaining the reasoning behind them. This lack of transparency raises concerns about the fairness and reliability of the grading process. Explainable AI (XAI) offers a solution to this problem by providing insights into how AI systems make decisions, thereby increasing trust and understanding among users.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to AI in Education
2.2 Automated Essay Grading Systems
2.3 Explainable AI
2.4 Importance of Explainable AI in Education
2.5 Existing XAI Techniques
2.6 Challenges in Implementing XAI in Automated Essay Grading
2.7 Studies on XAI in Education
2.8 Ethical Considerations in XAI
2.9 Comparison of XAI Systems

Chapter 3: System Design and Methodology
3.1 Introduction to System Design
3.2 Data Collection and Preprocessing
3.3 Feature Extraction Techniques
3.4 Machine Learning Algorithms
3.5 XAI Models
3.6 Evaluation Metrics
3.7 User Interface Design
3.8 Testing and Validation

Chapter 4: System Implementation
4.1 Introduction to System Implementation
4.2 Software Development Environment
4.3 Data Integration
4.4 Model Training and Testing
4.5 XAI Integration
4.6 User Testing
4.7 Performance Evaluation
4.8 System Optimization

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Future Research Directions
5.5 Conclusion

Thesis Overview on Explainable AI for Automated Essay Grading

Explainable AI (XAI) has emerged as a critical area of research in the field of automated essay grading. This thesis aims to explore the importance of explainability in AI systems, particularly in the context of education. The introduction provides a background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review delves into the existing literature on AI in education, automated essay grading systems, XAI, and its applications in education. The system design and methodology chapter outline the design and implementation of an XAI system for automated essay grading, including data collection, feature extraction, machine learning algorithms, XAI models, and evaluation metrics. The system implementation chapter details the software development environment, data integration, model training and testing, XAI integration, user testing, performance evaluation, and system optimization. The conclusion and summary chapter summarizes the findings, contributions, implications for practice, future research directions, and concludes the thesis.

Overall, this thesis aims to contribute to the growing body of literature on XAI in education and automated essay grading, and provide insights into the potential benefits and challenges of implementing XAI systems in educational settings. By increasing transparency and understanding in AI systems, educators and students can have greater trust in the grading process and improve learning outcomes.

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