Digital Competence Development Through AI-Supported Teaching Models in Higher Education: A Quasi-Experimental Study

Artificial intelligence (AI) has progressively transformed higher education by enabling personalized and interactive learning experiences. However, there is still limited empirical evidence on the effectiveness of structured AI-supported pedagogical models in the development of digital competence. The objective of the study was to determine the impact of an AI-supported teaching model on the development of digital competence in university students. A quasi-experimental design with pretest–posttest and a control group was employed, with a sample of 120 students. The experimental group participated in a structured instructional model based on the guided use of generative AI tools, while the control group followed a traditional approach. A validated instrument based on the DigCompEdu framework was used, evaluating five dimensions: Information literacy, digital communication, content creation, security, and problem solving. The results showed statistically significant improvements in all dimensions in the experimental group compared to the control group (p < 0.001). Mean scores increased from approximately 3.0 to 3.8 in the experimental group, while the control group showed minimal improvements. High effect sizes were identified (Cohen’s d = 1.7–1.8), indicating a significant impact of the intervention. In conclusion, structured AI-supported pedagogical models significantly improve digital competence in higher education, highlighting the importance of integrating AI through guided and well-founded pedagogical approaches.