Exploring Opportunities and Challenges of Artificial Intelligence in College Students for Personalized Training

Abstract

Artificial intelligence (AI) has empowered personalized training for each higher-education student. This study's meta-analysis and computational evaluation of AI frameworks understand the impact of AI-driven learning models on Student Engagement, Aggregation Rate, and Learning Efficiency. Findings show that AI-based learning can significantly contribute to better educational outcomes than conventional methods. The engagement rates were upped to 77%, and hybrid models share the highest rates at 85.3% versus traditional learning models at 69%. Regardless, 85.6% of the knowledge was retained immediately, and 74.2% was still retained after four weeks of training, with conventional training delivering only 72.1% and 54.3% of retention, respectively. Additionally, improving learning efficiency by 28.9 reduced the course completion time from 14.5 to 10.3 hours.