Research on the Impact of Artificial Intelligence on College Students' Learning Based on the Entropy Power Method

Abstract

With the development of society，artificial intelligence [1] has gradually penetrated into all aspects of our lives, and has had different degrees of impact on different aspects of society. The development of artificial intelligence has a different degree of impact on all aspects of society and also affects the learning life of college students. In order to understand the impact of artificial intelligence on college students' learning at different levels, this paper designs a questionnaire to interview and analyse it, obtains the survey feedback results and analyzes and evaluates the results according to the survey. We first transformed the questionnaire data from textual data to numerical data，and invalid sample exclusion through case analysis. A total of 145 invalid samples were excluded to avoid errors in the post analysis. The data were validated for reliability analysis of this non-scale question based on the Cronbach_alpha coefficient. After that, determining the purpose of their own indicators - the impact of AI on students' learning, based on the purpose of the selected indicators, the questions in the questionnaire were screened, and questions that were considered to be the same indicator were grouped together to create "proficiency in learning software", "Dependence on AI learning tools", "Demand for the use of AI learning tools", "Tolerance for the use of AI learning tools", and "Acceptability of the future direction of educational development of AI" five indicators, and separately homogenise and assign points to the various indicators constituted by the selected questions, and finally form a complete evaluation system. We analysed the validity of all the questions in the questionnaire to derive the weights of the questions within the indicator. Calculate the evaluation value of each indicator, then differentiate the five indicators constructed in the second question into positive and negative indicators. An analysis of the weights of the five indicators based on the entropy weighting method reveals that the weight of the indicator "proficiency in learning software" is as high as 51 per cent. Finally, the five indicators are combined to calculate a composite evaluation value for the sample. Using Origin to make a comprehensive evaluation value line graph of the sample, analysing both the fluctuation of the value of the lines and the degree of intensity, and finally concluding that AI has a wide range of influence and a large degree of influence on college students' learning.