Design and Evaluation of the Framework for Content Recommendations for Adaptive Learning based on Improved Genetic Algorithm

Abstract

Intelligent recommendation systems that can tailor material delivery to each learner's needs are desperately needed in the current educational environment due to the deluge of digital learning resources. Due to their incapacity to manage scant data, cold-start scenarios, and the dynamic growth of student learning preferences, traditional recommendation techniques—such as collaborative and content-based filtering—frequently fail in educational contexts. This study offers a thorough evaluation and performance analysis of a content recommendation system based on genetic algorithms (GA), created especially to improve individualized learning in online learning environments. Using a multi-objective fitness function that takes into account pedagogical relevance, engagement, diversity, and learner feedback, the GA model evolves each candidate solution (a series of suggested educational resources) through iterative selection, crossover, and mutation. This approach views content recommendation as an optimization problem. The framework's capacity to adaptively suggest learning resources across a range of academic areas was assessed through extensive testing on benchmark datasets and simulated learner profiles. According to the findings, the GA-based method performs better in terms of accuracy, novelty, and user satisfaction than both conventional and deep learning-based recommenders. Furthermore, even with little historical data, the system showed great promise in solving the cold-start issue and offering balanced, context-aware learning pathways. The recommendations were viewed as more stimulating, relevant, and in line with personal learning objectives, according to user research. The GA model's flexibility also makes it possible to integrate it with intelligent tutoring programs, adaptive tests, and real-time learning platforms. To sum up, the suggested GA-based framework supports more efficient, interesting, and learner-centric digital learning environments by offering a scalable and interpretable approach for personalized content recommendation in education. Future research will include contextual integration, hybrid GA models, and multi-objective optimization for equitable and inclusive learning paths.