The Impact of Deepseek on Academic Satisfaction of Art Majors - - Analysis based on SPSS

Abstract

This study used SPSS to conduct a rigorous data analysis to explore the impact of DeepSeek-based instruction on the academic satisfaction of university art students. Against the backdrop of limitations in traditional art education, the potential of integrating artificial intelligence technology into art teaching was explored. Third-year art students were divided into an experimental group and a control group of 40 students each, and the effectiveness of DeepSeek was assessed using independent samples t-tests, regression analyses, and correlations. The results showed that, in terms of personal, hardware, and pedagogical aspects, students in the experimental group had significantly higher academic satisfaction, with post-experimental scores of 18.75 ± 1.44, 18.60 ± 2.17, and 17.12 ± 1.93 respectively, compared to scores of 17.56 ± 2.23, 16.34 ± 2.41 and 15.00 ± 2.15 in the control group. These findings emphasise the effectiveness of DeepSeek-based instruction in increasing student engagement and satisfaction, providing empirical evidence for the digital transformation of arts education.