A Study on the Evaluation of AI's Impact on University Students' Learning Based on Data Analysis and Machine Learning

Abstract

With the rapid development of artificial intelligence (AI) technology, its application in the field of education has significantly affected the learning style of college students. This article explores the impact of artificial intelligence on college students' learning. It uses optimized evaluation algorithms and survey analysis to demonstrate AI's positive effects. The study begins with cleaning and correcting survey data, identifying textual features, and encoding variable types. After standardizing the data using the Min-max method, validity tests and descriptive statistical analysis were conducted. The results indicate high reliability and correlation in students' attitudes towards AI tools and their learning styles. An evaluation system was constructed based on these findings, selecting 11 key indicators through the Fisher algorithm model and assessing them with the LightGBM quantitative model. The model shows that AI influences college students' learning primarily through factors like learning purposes, safety of tool use, motivation, usage time, and satisfaction. This evaluation system not only confirms AI's positive impact but also offers optimization goals and methods to enhance students' learning abilities in the AI era.