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Introduction

Developing an AI system for reading comprehension has become a topic of great interest in the field of artificial intelligence and natural language processing. As technology continues to advance, the ability of machines to understand and interpret text has become increasingly important. Reading comprehension is a complex task that involves understanding the meaning of a given text, accurately answering questions based on that text, and drawing inferences from the information provided.

This thesis aims to develop an AI system that can effectively read and comprehend text, and accurately answer questions based on that text. The system will use advanced natural language processing techniques and machine learning algorithms to achieve this goal. By developing such a system, we hope to contribute to the advancement of AI research and improve the capabilities of machines to understand and interpret human language.

Chapter One: 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 Two: Literature Review
2.1 Overview of Reading Comprehension
2.2 Natural Language Processing Techniques
2.3 Machine Learning Algorithms
2.4 Previous Studies on AI Reading Comprehension Systems
2.5 Challenges in Developing AI Reading Comprehension Systems
2.6 Evaluation Metrics for Reading Comprehension Systems
2.7 State-of-the-Art AI Reading Comprehension Systems
2.8 Future Trends in AI Reading Comprehension Research
2.9 Gaps in Existing Literature
2.10 Summary of Literature Review

Chapter Three: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Feature Extraction
3.4 Model Selection
3.5 Training and Evaluation
3.6 Hyperparameter Tuning
3.7 Error Analysis
3.8 Ethical Considerations

Chapter Four: System Implementation
4.1 Software and Tools
4.2 Data Acquisition
4.3 Model Development
4.4 System Integration
4.5 Performance Evaluation
4.6 Results and Discussion
4.7 Comparison with Existing Systems
4.8 Future Enhancements

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Future Research
5.4 Limitations of the Study
5.5 Conclusion

Overall, this thesis aims to contribute to the field of AI research by developing an advanced system for reading comprehension. By combining the latest advancements in natural language processing and machine learning, we hope to create a system that can accurately understand and interpret text, leading to significant improvements in AI capabilities.

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