Knowledge, perceptions, and applicability of universal design for learning and artificial intelligence in inclusive education

Abstract

Universal Designfor Learning (UDL) and Artificial Intelligence (AI) are established frameworks that can contribute to the development of more inclusive educational environments. Assessing the level of training and the perceptions of future teachers is essential for ensuring their proper implementation. The aim of this paper was to analyze university students’ knowledge, understanding, and perceived applicability of UDL and AI in relation to inclusive education. A multi-method study was developed with quantitative predominance and qualitative support. The sample consisted of 135 university students enrolled in degree or master's programs in education, who were selected by non-probabilistic convenience sampling. A structured questionnaire was used that included closed Likert-type items and open questions. A descriptive analysis of frequencies and means was performed for the quantitative variables, together with an inductive content analysis for qualitative responses, including frequency analyses of categorical variables. The instrument showed high reliability (α = .863) and adequate instrument validity (KMO = .839; Bartlett p < .001). The data analysis showed that university students have limited knowledge of UDL, especially in relation to its guidelines and didactic application, although university students recognize its inclusive potential. Regarding AI, there is a predominance of a positive perception linked to the personalization of learning, accessibility, and design of educational resources, alongside concerns about technological dependence, misinformation, inequalities of access, and loss of the human component. The lack of specific training in this area emerges as a key barrier preventing these resources from being properly applied in the classroom. Taken together, the findings of this study show that future teachers are at an early stage of engaging with the UDL framework and AI, showing interest and sensitivity towards inclusion, but also revealing significant training gaps. This reinforces the need to systematically incorporate both contents into university training programs, fostering professional competencies which integrate technological innovation with the principles of educational equity.