Project Read // Comprehension Question Generator
Josh Allan
Frontend Engineer
Software Engineer
Web Developer
ChatGPT
Next.js
TypeScript
Overview
Target User: Educators (K-8 Teachers)
Objective: Build a web-based tool that generates comprehension questions based on any input text, tailored for K-8 students. The generator uses advanced inputs to customize question complexity, align with Common Core standards, and adapt output based on text type and user preferences.
Project Description
The Comprehension Question Generator allows teachers to input or paste a text passage and receive a set of comprehension questions tailored for their students. The tool aims to enhance learning and comprehension by creating questions aligned with Common Core standards, with optional customization features to adjust question types and formatting.
Key Features
Text Input Section
Text box with a 450-word limit.
Example passage: “Mom and Dom baked a cake together. After baking, they went to the lake to celebrate. They swam, played games, and ate their cake.”
Grade Level Selection (Dropdown)
Options range from Kindergarten to Grade 8.
Example: "Grade 2"
Advanced Settings
Number of questions (default: 4, max: 6).
Option to include multiple-choice questions (checkbox, default unchecked).
Text Type Selection (dropdown): Literature or Informational Text.
Common Core Standards (checkboxes): Display dynamically based on grade level and text type.
Integration with LLM (Large Language Model)
Utilizes LLMs with customized prompts based on user-selected settings.
Temperature and structured output settings for consistent responses.
Output Customization
Generated questions adhere to chosen Common Core standards and grade-appropriate complexity.
JSON-structured output for easy parsing and display.
Skills & Technologies Utilized
Frontend Development: React.js for dynamic forms and input handling.
Backend Integration: Node.js with API endpoints for processing requests and interacting with the LLM.
Styling: EmotionCSS for consistent UI components and responsive design.
Validation: Input validation to ensure appropriate text length, grade selection, and standard applicability using Zod.
LLM Prompt Engineering: Crafting prompts to leverage LLM capabilities for structured educational content generation.
Accessibility & UX Design: Ensured usability for teachers with intuitive form elements, tooltips, and descriptive field labels.
Challenges & Solutions
Challenge: Handling variable grade levels and Common Core standards dynamically.
Solution: Developed a dynamic mapping system to display appropriate options based on user selections.
Challenge: Generating consistent LLM responses tailored to educational standards.
Solution: Crafted structured user and system prompts, and maintained output format consistency using JSON.
This project exemplifies a strong focus on educational tools, form validation, dynamic input management, LLM integration, and standards-driven output for impactful learning outcomes. The Comprehension Question Generator demonstrates expertise in frontend and backend development, API integrations, and intelligent content generation using large language models.
Comprehension question generator on initial load.
Comprehension question generator after content has been generated.
Exported pdf for students.
Exported pdf for teachers.
