Using factor analysis method to evaluate the impact of artificial intelligence on college students' learning. By using SPSS software to test the reliability and validity of the questionnaire, key factors were selected and a factor analysis evaluation model was constructed, revealing the key factors that affect the understanding of artificial intelligence among science and engineering freshmen. This study explores students' behavior and attitudes towards the use of artificial intelligence learning tools, including time investment, proportion of using artificial intelligence tools to complete assignments, participation in online activities, use of artificial intelligence learning tools, perspectives on replacing teachers with artificial intelligence tools, and recognition of the advantages of learning software over traditional classroom teaching. By analyzing the factor load matrix and scores, the importance of each factor can be explained. These technologies provide in-depth insights into the impact of artificial intelligence. The establishment of the model reveals the key factors that affect college students and obtains ratings for each factor. Finally, a rating table was generated and compared, and the results were evaluated and analyzed. In summary, through factor analysis, we can comprehensively evaluate the impact of artificial intelligence on college students' learning and provide useful references for further research and educational practice.

Zhe Guo, Tong Jing, Haichao Zhu, Xuejiayi Xu. Analysis of the Impact of Artificial Intelligence on College Students' Learning. The Frontiers of Society, Science and Technology (2023) Vol. 5, Issue 13: 110-116. https://doi.org/10.25236/FSST.2023.051318.

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