Key points are not available for this paper at this time.
With the rapid development and increasing popularity of AI in various industries, its impact on higher education, especially on the learning experience of college students, has become more profound. Various AI-powered educational tools and smart learning software are emerging, providing students with rich and convenient learning resources. The selected impact indicators were analyzed using the K-medoids clustering algorithm, which classified them into five different clusters: attitudes and expectations towards the use of AI learning tools, future prospects and adaptability of AI learning tools, patterns and purposes of AI use, safety and related concerns of AI tools, and meaningful and desirable features of AI learning tools. Subsequent ANOVA tests yielded a p-value of less than 0.05, thus confirming the appropriateness of the selected evaluation metrics. This academic review highlights the sound selection of evaluation criteria in the context of AI educational applications.
Wen et al. (Thu,) studied this question.