Review And Analysis of Genetic Algorithm based Content Recommendation Framework

Abstract

In the modern educational landscape, the overwhelming abundance of digital learning resources has created an urgent need for intelligent recommendation systems that can personalize content delivery to meet individual learner needs. Traditional recommendation approaches—such as collaborative and content-based filtering—often fall short in educational settings due to their inability to handle sparse data, cold-start scenarios, and the dynamic evolution of student learning preferences. This paper presents a comprehensive review and performance analysis of a Genetic Algorithm (GA)-based content recommendation framework, specifically designed to enhance personalized learning in digital education platforms. The GA model treats content recommendation as an optimization problem, where each candidate solution (a sequence of recommended educational resources) is evolved through iterative selection, crossover, and mutation, based on a multi-objective fitness function that incorporates pedagogical relevance, engagement, diversity, and learner feedback. Extensive experiments were conducted on benchmark datasets and simulated learner profiles to evaluate the framework’s ability to adaptively recommend learning materials across diverse academic subjects. The results show that the GA-based approach outperforms traditional and deep learning-based recommenders in terms of accuracy, novelty, and user satisfaction. Moreover, the system demonstrated strong potential in addressing the cold-start problem and providing balanced, context-aware learning paths even with minimal historical data. A user study further confirmed that the recommendations were perceived as more relevant, motivating, and well-aligned with individual learning goals. The adaptability of the GA model also allows integration with real-time educational platforms, intelligent tutoring systems, and adaptive assessments. In conclusion, the proposed GA-based framework represents a scalable and interpretable solution for personalized content recommendation in education, supporting more effective, engaging, and learner-centric digital learning environments. Future work will explore hybrid GA models, contextual integration, and the use of multi-objective optimization for inclusive and fair learning pathways.