The use of Artificial Intelligence (AI) in Higher Education (HE) is an expanding reality, exerting an increasingly significant influence on teaching and learning processes. Its integration into the university environment is reshaping how instructors design and deliver their courses while providing students with new opportunities to personalize their learning experiences. Not only does AI enable task automation and resource optimization, but it also presents methodological, technological, and ethical challenges that warrant thorough investigation. In this context, we assessed the academic performance of two hundred and thirty eight students enrolled in an undergraduate degree program for early years education teachers, defining performance as their ability to design learning situations aimed at promoting mathematical thinking in young children. Our analysis distinguished between those who used AI to complete the task and those who relied on traditional teaching resources. To this end, we adopted a sequential explanatory mixed-methods design. In the quantitative phase, we employed a quasi-experimental post-test only design with experimental and control groups. In the qualitative phase, we conducted an in-depth analysis of the learning materials produced by the students in the experimental group with the support of AI. The findings indicate that the application of AI brought about significant changes in the teaching-learning process. The experimental group obtained better academic results than the control group. These results underscore, on one hand, the transformative potential of AI to improve pedagogical practices, and on the other, the need for research to examine its long-term effects on student learning, teacher engagement and ethical use, and the development of HE curricula that include AI as a teaching resource.
Mariñoso et al. (2025) studied this question.