Learning programming in higher education faces significant challenges, including high dropout rates and difficulties in understanding abstract concepts. Previous studies have explored various teaching methods, but the effectiveness of Generative Artificial Intelligence (GenAI) in this context has not yet been widely investigated. This study compares the efficacy of GenAI with video-based active learning methods for teaching programming to university students. Through an experimental design with 40 computer engineering students, academic performance, perception of usefulness and ease of use, and satisfaction and motivation were evaluated. The results showed no statistically significant differences between the groups in academic performance, perception of usefulness and ease of use, or satisfaction and motivation. Both methods proved equally effective in improving learning and maintaining student motivation. These findings suggest that GenAI can be a viable alternative to traditional methods, offering opportunities to diversify pedagogical strategies in programming education. Educators are encouraged to consider integrating GenAI to complement existing methods, ensuring implementation that maintains high levels of perceived control, immersion, and curiosity among students.