Personalized College English Learning Experience Assisted by Artificial Intelligence: An Algorithm-Driven Adaptive Learning Approach

Personalized College English Learning offers customized education based on individual necessities and aims. Students enhance their linguistic ability efficiently over engaging sessions, specific curricula, and professional teachers. This study aims to progress a smart Artificial Intelligence (AI)-based adaptive learning method for enhancing personalized college English learning experience. Our proposed model provides an intelligent detecting device-enabled setting for learning English over big data analysis and machine learning based on a DM approach. Intelligent sensing strategies detention significant statistics from college English students and big data analysis examines the data to produce beneficial insights that increase real-time personalizing of English learning experiences. We suggest an innovative Starling Murmuration fine-tuned Dynamic Weighted Random Forest (SM-DWRF) system for classifying the data. Our model influences perceptions from collective behavior in starling flocks to dynamically alter feature weights in DWRF arrangement. It integrates fine-tuning mechanisms to adaptively weight features, developing classification accurateness and strength in adapted college English learning settings. We implemented our suggested model in Python software. In the assessment stage, we accurately measure the effectiveness of our proposed SM-DWRF model in identifying diverse features of English learning across various parameters. Our experimental findings incontestably showcase the greater performance of our model associated with conventional approaches in classifying content from multimodal statistics. Significantly, we perceive prominent enhancements in reliability and robustness, when acclimating to dynamic learning settings.