This study investigates the impact of generative artificial intelligence (GenAI) on engineering students’ creativity, examining the mediating roles of critical thinking and AI self-efficacy in this relationship. We analyze the data collected using SPSS (v.24) and SmartPLS (v.4) to assess the variables’ structural relationships and effect sizes. The results demonstrate that GenAI usage competence significantly enhances engineering students’ creativity (t > 1.96, p < 0.05,f² = 0.012). Furthermore, critical thinking partially mediates the relationships between GenAI usage competence and AI self-efficacy, as well as between GenAI usage competence and creativity (t > 1.96, p < 0.001). AI self-efficacy is also a partial mediator in the relationships between critical thinking and creativity, and between GenAI usage competence and creativity (t > 1.96, p < 0.05). This study identifies a chain mediation model in which critical thinking and AI self-efficacy sequentially mediate the relationship between GenAI usage competence and student creativity (t > 1.96, p < 0.001). These findings highlight the interplay between technological tools, cognitive abilities, and psychological factors, indicating that simply teaching technical skills is not enough. For engineering education, this implies that integrating GenAI tools into the curriculum must go hand in hand with fostering students’ critical thinking and AI self-efficacy to truly enhance creativity.

The purpose of this study is to examine whether there is a relationship between academic skills and Microsoft Mathematics Visualization as a result of the use of Microsoft Mathematics Visualization in the classroom. Specifically, when it comes to academic skills, Microsoft Mathematics Visualization is considered an independent variable, and academic skills are considered a dependent variable that has been influenced by the negative or positive characteristics of various instructional methodologies. The quantitative research approach has been used in the development of this document. Approximately 300 responses were collected during data collection, and, following correction, the sample size was decreased to 268 responses, which were mined in SPSS to generate the results. In the findings of the model summary, it is shown that the selected model has a very good above-average value of adjusted R², suggesting a good fit, in accordance with the results of model summary. Microsoft Mathematics Visualization is regarded to be a predictor for academic skills since its coefficient of 0.794 on conversion explains up to 79.4 percent of the variance of the model when converted. We have B=0.892 as the value of the predictors beta in our first regression. For example, a one-unit change in Microsoft Mathematics Visualization will result in an 89.2 percent change in academics skills, which may be interpreted as follows: Aside from that, the correlation coefficient between Microsoft Mathematics Visualization and student caliber (r=0.840) indicates a positive relationship between student caliber and Microsoft Mathematics Visualization. 0.963 indicates that formal schooling has been significantly influenced by Microsoft Mathematics Visualization training, which might be further elaborated as that formal education has been boosted by Microsoft visualization training, according to the results of the test.