LearnWithCresvia: AI Powered Gamified learning and Coding Platform for Engineering Students

Abstract

Engineering students frequently encounter challenges such as academic overload, fragmented study resources, and inconsistent coding practice, which collectively hinder effective learning and productivity. The separation of syllabus management, previous year question papers, and skill development activities often leads to inefficiencies and confusion. This paper proposes an integrated learning platform designed to address these challenges by unifying academic management, structured coding practice, and intelligent exam preparation within a single system. The platform systematically organizes learning resources, including syllabus topics and past examination papers, while incorporating coding challenges aligned with academic requirements to promote continuous skill development. Furthermore, the system employs a data-driven approach to track user performance and generate personalized insights, enabling students to identify weak areas and optimize their study strategies. By providing a centralized and adaptive learning environment, the proposed solution aims to enhance focus, improve problem-solving capabilities, and facilitate efficient exam preparation. The implementation of this platform demonstrates the potential to significantly improve learning outcomes by bridging the gap between academic knowledge and practical skill development.

Introduction

This paper proposes LearnWithCresvia, an AI-powered integrated learning platform designed to improve engineering education through adaptive gamification, machine learning, and personalized learning. Traditional educational approaches often separate syllabus management, coding practice, and exam preparation, leading to fragmented learning, reduced productivity, and lower student engagement. The proposed system addresses these challenges by combining all learning activities within a single platform.

The platform leverages Artificial Intelligence (AI) and Machine Learning (ML) to analyze student behavior, performance, response times, and engagement patterns. Based on this analysis, it dynamically personalizes learning content, coding challenges, study plans, and reward mechanisms. Adaptive gamification elements such as leaderboards, badges, experience points (XP), streaks, and personalized challenges are incorporated to enhance motivation, participation, and learning effectiveness.

The system consists of several key modules: an Academic Learning Hub for syllabus content and previous-year questions, a Coding Arena with real-time code execution and AI feedback, an AI Focus Engine that identifies high-priority topics and creates personalized study plans, a Reference Zone containing external learning resources, and a Growth System that manages gamification features.

Technically, the platform is built using React and Ionic for the frontend, Supabase/Firebase for backend services, Judge0 API for code execution, and OpenAI APIs for intelligent feedback and recommendations. ML techniques such as performance prediction, learner clustering, recommendation systems, reinforcement learning, and AI-based feedback generation enable adaptive learning and personalized support.

The study aims to evaluate the impact of AI-powered gamification on learning outcomes, student engagement, performance prediction, satisfaction, and the influence of learner-specific factors. By integrating academic management, coding practice, and intelligent exam preparation into a unified ecosystem, the proposed platform seeks to improve academic performance, increase engagement, and better prepare students for real-world engineering challenges.

Conclusion

This paper presented LearnWithCresvia, an AI-powered integrated learning platform designed to address key challenges in engineering education, including academic overload, fragmented resources, and inconsistent coding practice. By combining academic management, structured coding challenges, and intelligent exam preparation within a unified system, the proposed platform offers a comprehensive and efficient learning solution. The integration of machine learning techniques and adaptive gamification enables personalized learning experiences tailored to individual student needs. The system effectively analyzes learner behavior, predicts performance, and dynamically adjusts content, difficulty levels, and motivational elements. Experimental results demonstrate significant improvements in academic performance, coding accuracy, and student engagement when compared to traditional learning approaches. The use of data-driven insights and real-time feedback further enhances learning efficiency and supports continuous skill development. Despite these promising outcomes, certain limitations exist. The current evaluation is based on a limited sample size and short-term analysis, which may not fully capture long-term learning behavior and knowledge retention. Additionally, challenges related to data privacy, scalability, and model transparency need to be addressed for large-scale deployment. Future work will focus on extending the platform with advanced deep learning models for more accurate predictions and recommendations. The inclusion of multi-modal learning support, such as code visualization and interactive simulations, will further enhance user experience. Additionally, integrating collaborative features like peer learning, coding competitions, and discussion forums can foster a more engaging learning environment. Longitudinal studies will also be conducted to evaluate the long-term impact of adaptive gamification on student performance and career readiness. In conclusion, the proposed system demonstrates the potential of combining artificial intelligence, machine learning, and gamification to transform engineering education into a more personalized, engaging, and effective learning experience.

References

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Copyright

Copyright © 2026 Backialakshmi J, Gayathiri G, Poorani R, Vino Pabiyana L. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.