Impact of generative AI on university students’ digital competences: experimental evidence based on the DigComp framework

Abstract

This study analyzes the impact of the formative use of generative artificial intelligence (AI) on the development of digital competencies in university students. The intervention was implemented through a randomized controlled trial research design. The experimental group received training aimed at strategically using generative AI models to complete academic tasks, while the control group carried out the same activities without specific AI guidance. The impact was assessed using a difference-in-differences model with fixed effects, based on pre- and post-intervention questionnaires. Competences were analyzed according to the European DigComp 2.2 framework, covering four main competence areas: information and data literacy, communication and collaboration, safety, and problem solving. The results show statistically significant improvements in information and data literacy and in problem solving, both in their functional and metacognitive dimensions. Differential effects were also identified depending on the initial level of digital competence, with more pronounced gains among students with lower prior proficiency, who showed significant progress across all evaluated competencies. These findings suggest a compensatory effect of the didactic use of AI, capable of reducing gaps and promoting more equitable and inclusive learning processes. The study supports the guided integration of emerging technologies in higher education to strengthen digital competencies.