We are experiencing a serious health crisis due to COVID‐19 that has a major impact on the field of education. The educational system therefore needs to be updated and innovated, with the addition of digital resources, to adapt the teaching and learning processes to students with disabilities. To meet the goal of high‐quality education, teachers must have adequate digital competence to face the educational demands that are placed on them. Therefore, the purposes of this study are: to know the teachers' knowledge about digital resources to support students with disabilities (O1); at each educational stage (O2), identify the variables that have a significant impact on the acquisition of teacher competence (O3); propose a selection of useful ICT resources for each type of disability (O4). An ex post facto design was used with 1194 teachers from Andalusia (Spain). The results showed the medium‐low level of the teaching staff, especially in the higher education stage. In addition, gender, motivation, attitude and having students with special needs are determining factors in the development of teacher knowledge. The results highlight the urgent need for teachers to be trained in digital resources. We hope that the range of resources proposed in this study will help teachers enhance their teaching practice. Practitioner notes: What we already know about this topic Teachers have to find and adapt educational technology to achieve a higher level of success in students with disabilities. The use of ICT resources by teachers acts as a bridge to promote learning in students with disabilities. What this paper adds Identification of those predictors that significantly influence the acquisition of digital competence in teaching staff at various stages of education: gender, educational stage, motivation, attitude towards technologies and having students with specific educational support needs. A proposal of didactic applications is provided to address the different types of functional diversity. Implications for practice and/or policy It is essential that training plans on digital competence are established to assist students with functional diversity in their different learning stages. In this sense, personalised training itineraries or TMOOCs can be of major assistance. Expansion of the study sample (macro level) to identify digital factors, which affect the acquisition of the digital competence of the teaching staff to support their students' disabilities. Change regarding the type of diagnosis through self‐perception tests, such as those presented by these models, and subsequent work in other fields such as e‐portfolios of evidence or instruments in the form of problem solving.

This study aimed to investigate how AI impacts students’ intrinsic motivation and learning experiences in various cultural and academic settings. An analysis of the effects of AI-powered learning tools on intrinsic motivation and learning enhancement was conducted in early 2024. The study recruited 455 students (192 males and 263 females) from Egypt, Saudi Arabia, Spain, and Poland who were studying diverse majors in educational faculties. Using a 5-point Likert scale, the validated instrument included three dimensions with a total of 10 items: intrinsic motivation and AI, AI and enhancing learning, and overall perception of AI in education. Significant differences were found by nationality and major, with Polish students and education technology majors showing the highest motivation levels. As a result of enhancing learning experiences, autonomy, and critical thinking skills, AI tools were found to positively influence motivation. Motivation, however, was not significantly influenced by academic level. Despite ethical concerns, students held a moderately positive view of artificial intelligence in education. Further research on optimal AI implementation and ethical considerations in education is necessary based on these findings.

The aim of this research is to analyse the attitudes towards information and communication technologies (ICT) of higher education teachers from an affective, cognitive and behavioural model for teaching and research. It also aimed to explore different factors that can predict such attitudes. A non-experimental study was proposed using a survey technique and descriptive and inferential analyses were carried out using a multiple linear regression model (MLR). In total, the sample was formed by 867 university professors from Spain belonging to different areas of knowledge. The results show that these teachers have a medium total attitudinal level, so the lowest attitudes have been represented by the behavioural ones, followed by the affective ones. Regarding the predictor variables, variables that can predict such attitudes were found to be age, participation in projects, gender and teaching in face-to-face and/or online universities (ordered from highest to lowest priority).