Generative artificial intelligence (GenAI) is redefining higher education by enabling adaptive and creative learning environments. This study presents a framework applied in a pilot course on Algorithms and Programming, which combines automated skill diagnostics, team formation through classification algorithms (K Nearest Neighbors), content personalization, and continuous feedback based on large language models (LLMs), implemented through the MAKE platform. The results show a significant improvement in learning efficiency and quality: GenAI-assisted teams reduced delivery times by up to 50% in key stages, and 80% achieved functional prototypes, compared to 45% in the control group. Additionally, the adaptive environment fostered creativity by supporting ideation, autonomy, and personalized exploration. This model demonstrates how GenAI can transform teaching in technical fields such as engineering by combining academic performance with creative development. The study also addresses challenges related to technological equity, data protection, and the need for teacher training to ensure ethical and effective implementation. The proposed framework represents a strong pathway toward more personalized, innovative, and student-centered education.