Before the rise of advanced AI models, automation still had the power to solve complex tasks through smart design and decision-making. One such project I developed is a Python-based intelligent quiz solver—an automation that navigates to a quiz, analyzes questions, and answers them correctly using real-time web data.
Built entirely with Python, Selenium, and decision programming techniques, this solution achieved an 80% success rate and could complete a 60-question quiz in just 70 seconds—without any machine learning model involved.
Project Overview: Intelligent Answering Bot
The objective of this automation was to read, understand, and solve online quizzes automatically, even if the target application changed over time.
Core Functionality
- Launch & Navigate to Quiz Page
Uses Selenium to open the browser, access the quiz URL, and initialize interaction. - Smart Adaptability
If the structure of the quiz changes (e.g., layout, tags, element IDs), the bot adjusts its strategy using dynamic selectors and pattern recognition logic. - Read Questions from the Quiz
Extracts the quiz content dynamically using XPath/XML parsing and DOM traversal. - Answer Extraction from Web (Google Search)
The bot scrapes Google Search results using question text and analyzes snippets to identify the most probable correct answer. - Marking the Correct Answer
Based on keyword matching and logic scoring, the bot selects the most relevant answer. - Submit Quiz and Evaluate Score
After all questions are answered, it submits the form and retrieves the final result. - Logging and Reporting
Generates structured logs and XML reports capturing:
- Questions
- Chosen answers
- Google result excerpts
- Final score
Technologies Used
- Python – Core programming and logic handling
- Selenium WebDriver – Web interaction and automation
- XML & XPath – For parsing and data structure management
- Decision Programming (DP) – Logic tree for answer selection
- Google Search Parsing – Real-time data gathering from the web
Performance Metrics
- Success Rate: ~80% accuracy without AI/ML
- Average Time: 70 seconds for 60 questions
- Efficiency: ~1.2 seconds per question, including real-time web search and DOM parsing
Why This Project Was Innovative
At a time when AI models weren’t publicly accessible or widely available, this project demonstrated that intelligent automation could still:
- Adapt dynamically to UI changes
- Perform real-time web-based research
- Make decisions based on rule-based inference
- Deliver fast and consistent results across platforms
This project laid the foundation for data-driven automation and is a testament to what’s possible using well-structured logic and smart scripting.
Keywords:
Python quiz solver, automation before AI, Selenium Python project, Google scraping automation, Python web bot, smart automation script, decision programming, quiz automation Python, dynamic XPath parser, intelligent bot Python, pre-AI intelligent systems