The article “Impact of Generative AI on University Students’ Digital Competences: Experimental Evidence Based on the DigComp Framework” addresses a central issue in today’s higher education: how the guided use of generative artificial intelligence tools can contribute to the real development of digital competences among university students.
Against the still widespread idea that young people already master the digital world simply because they live surrounded by technology, the study reminds us that knowing how to use tools does not necessarily mean knowing how to use them critically, strategically, and autonomously.
Based on a randomized controlled trial with university students, the research analyzes the impact of a training intervention focused on the strategic use of generative AI. The results show significant improvements in competences related to information search and management, interaction with technological tools, and digital self-regulation.
Particularly relevant is the effect observed among students with a lower initial level of digital competence, suggesting that AI, when integrated with didactic guidance, can act as a compensatory tool capable of reducing gaps and fostering more equitable learning.
One of the main contributions of the study is that it does not present generative AI as an automatic solution, but rather as an educational resource that requires support, planning, and pedagogical judgment. The article emphasizes that the value of these technologies lies not only in their ability to produce answers, but also in how they can help students ask better questions, verify information, solve problems, and become aware of their own learning needs.
In this sense, generative AI appears not as a substitute for teachers, but as an opportunity to rethink university teaching from a more critical, inclusive perspective, oriented toward the development of competences for digital life.
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How to Cite: García, C. G., & Pallarés, N. (2026). Impact of generative AI on university students’ digital competences: experimental evidence based on the DigComp framework. RIED-Revista Iberoamericana de Educación a Distancia, 29(1), 53–77. https://doi.org/10.5944/ried.45533