Building an AI-Powered SAT Reading Practice App

Introduction

My journey into AI application development began with a simple yet compelling motivation: to build something meaningful using large language models. Given that these are fundamentally “language” models, I naturally gravitated toward applications involving language processing and understanding. Through discussions with colleagues, an interesting opportunity emerged - developing a question generation system for certification exams in Korea. I decided to start with English language tests, specifically the SAT Reading section, as a stepping stone toward Korean certification exams.

Understanding SAT Reading Patterns

The first phase involved thorough research using the College Board website to analyze example questions. I developed an AI agent specifically designed to analyze pattern recognition in SAT Reading questions, understanding the underlying structure and requirements that make these questions effective assessment tools.

AI Agent Architecture

The core of the application consists of three specialized AI agents working in concert:

1. The Question Generation Agent creates SAT-style reading comprehension questions based on the patterns identified in official SAT materials.

2. The Solution Agent processes and solves these generated questions, providing detailed explanations for each answer.

3. The Validation Agent ensures the quality and authenticity of both questions and solutions, maintaining alignment with SAT standards.

The development of effective AI agents for generating SAT Reading questions required careful attention to prompt engineering. My approach focused on leveraging few-shot learning to ensure the generated questions matched the style, complexity, and educational value of official SAT materials.

Initial Prompt Development

My first step involved analyzing numerous official SAT Reading questions to identify key patterns and characteristics. I observed that high-quality SAT questions typically follow specific structures, test particular skills, and maintain consistent difficulty levels. To capture these elements, I utilized GPT-4o to help refine my prompts.

• Role Definition: The prompt begins by establishing the AI agent’s role as an expert SAT question designer with deep knowledge of both College Board standards and educational assessment principles.

• Passage Construction Guidelines: For passage construction, I provided specific parameters that align with official SAT Reading section requirements

• Question Format Specifications: The question format section details the structural requirements and types of questions to be generated

• Answer Choice Design: The answer choice design section outlines the principles for creating effective multiple-choice options

Few-Shot Learning Implementation

The prompt engineering process began with carefully selected examples from official SAT materials. Each example served as a demonstration of the desired output format, question style, and complexity level. My prompt structure included:

1. Context Setting: Clear instructions about the SAT Reading format and requirements
2. Example Questions: 3-5 high-quality SAT Reading questions with their corresponding passages
3. Question Analysis: Detailed breakdown of what makes each example effective
4. Format Guidelines: Specific requirements for passage length, question complexity, and answer choices

Validation and Refinement

To ensure quality, I implemented a feedback loop where generated questions were evaluated against my validation criteria. The validation agent assessed:

• Alignment with SAT standards
• Question clarity and unambiguity
• Answer choice plausibility
• Reading level appropriateness
• Content relevance

Technical Implementation

For the mobile application development, I chose Flutter for its cross-platform capabilities and robust performance. The backend infrastructure utilizes Firebase, selected for its seamless integration capabilities with Flutter applications. The system employs Firebase Authentication for user management and Firebase Datastore for data persistence.

User Experience and Analytics

The application provides users with a comprehensive history of their solved questions. More importantly, the AI system analyzes user performance patterns to identify specific strengths and areas for improvement. This personalized analysis helps students focus their preparation efforts more effectively.

Conclusion

The development of the SAT Reading Prep Application demonstrates the power of AI in educational tools. By leveraging advanced language models and thoughtful prompt engineering, the app provides a personalized and effective learning experience. As AI technology continues to evolve, such applications will play a crucial role in transforming education, making it more accessible and tailored to individual needs.

The app is currently under review, and I will share a link to the application once it is approved. We invite you to explore the app and experience firsthand how AI can enhance your SAT preparation journey.