Construction of STEAM Personalized Online Learning System for Artificial Intelligence

Traditional online learning systems have long system response times and low accuracy in predicting learners' behavior, learning results, or personalized needs. In order to optimize the article on this issue, a STEAM personalized online learning system for artificial intelligence is constructed. This research adopts a combination of artificial intelligence technology and data analysis methods for system construction. First, the learner's personal information, learning behavior and learning results and other data are collected, and effective data analysis is carried out. Secondly, the application of personalized recommendation algorithms and intelligent learning models to recommend learning content, projects and activities suitable for learners' individual needs based on their interests, abilities and learning history. Through experimental tests, the mean square error of the system in this paper is maintained at 0.01-0.05, and the system can improve the user experience, learning effect and system performance.