Year: 2025
Type: article
Abstract: In the evolving landscape of software development, self-learning tools are becoming increasingly essential for developers aiming to improve their technical skills, problem-solving abilities, and readiness for job placements. AutoPrep is an AI-assisted self-learning platform designed to support aspiring and current developers by providing an interactive, intelligent, and personalized environment for technical preparation. Built using Python and Streamlit, and powered by Google’s Generative AI (Gemini), AutoPrep enables users to engage with dynamically generated quizzes, debug code with AI suggestions, and explore curated learning content aligned with industry demands. The platform integrates authentication, user profiling, and performance tracking using SQLite databases, ensuring a secure and persistent learning experience. It combines multiple AI-powered utilities, such as a quiz generator, code debugger, and contextual prompt-based guidance system, to offer comprehensive support across different stages of technical learning. By automating routine tasks like question generation and code analysis, AutoPrep significantly reduces the cognitive load on learners and provides instant, personalized feedback. This research outlines the architectural design, implementation strategy, and educational significance of AutoPrep in enhancing the efficiency of self-paced learning. Through real-time AI interaction, it bridges the gap between theoretical knowledge and practical application, empowering users to gain confidence in their coding abilities. AutoPrep demonstrates the potential of integrating modern generative AI tools with ed-tech platforms to create more engaging, scalable, and effective learning environments.
Authors: Mayank Jain, Nav Neet Pal, Mohd Shadab, Akshu Agarwal, Mohd Sadim
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4411290319