Technologies based on artificial intelligence are transforming teaching practices in higher education. However, many university faculty members still face difficulties in incorporating these tools in a critical, ethical, and pedagogically meaningful way. This review addresses the issue of limited artificial intelligence literacy among educators and the main obstacles to its adoption. The objective was to analyze the perceptions, resistance, and training needs of faculty members in the face of the growing presence of artificial intelligence in educational contexts. To this end, a narrative review was conducted, drawing on recent articles from Scopus and other academic sources, prioritizing empirical studies and reviews that explore the relationship between intelligent systems, university teaching, and the transformation of academic work. Out of 757 records initially retrieved, nine empirical studies met the inclusion criteria. The most frequently examined tools were generative artificial intelligence systems (e.g., ChatGPT), chatbots, and recommendation algorithms. Methodologically, most studies employed survey-based designs and thematic qualitative analysis. The main findings reveal a persistent ambivalence: faculty members acknowledge the usefulness of such technologies, but also express ethical concerns, technical insecurity, and fear of professional displacement. The most common barriers include lack of training, limited institutional support, and the absence of clear policies. A shift in the teaching role is observed, with greater emphasis on mediation, supervision, and critical analysis of output generated by artificial intelligence applications. Additionally, ethical debates are emerging around algorithmic transparency, data privacy, and institutional responsibility. Effective integration in higher education demands not only technical proficiency but also ethical grounding, regulatory support, and critical pedagogical development. This review was registered in Open Science Framework (OSF): 10.17605/OSF.IO/H53TC.

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

Non-invasive, non-pharmacological interventions utilizing virtual reality (VR) represent a promising approach to enhancing cognitive function in patients with degenerative cognitive disorders. Traditional “pen and paper” therapies often lack the practical engagement in everyday activities that older individuals encounter in their environment. These activities pose both cognitive and motor challenges, underscoring the necessity of understanding the outcomes of such combined interventions. This review aimed to assess the advantages of VR applications that integrate cognitive-motor tasks, simulating instrumental activities of daily living (iADLs). We systematically searched five databases–Scopus, Web of Science, Springer Link, IEEE Xplore, and PubMed, from their inception until January 31, 2023. Our review revealed that motor movements, coupled with VR-based cognitive-motor interventions, activate specific brain areas and foster improvements in general cognition, executive function, attention, and memory. VR applications that meld cognitive-motor tasks and simulate iADLs can offer significant benefits to older adults. Enhanced cognitive and motor performance can promote increased independence in daily activities, thereby contributing to improved quality of life.

The COVID-19 pandemic has changed people’s lives and the way in which certain services are provided. Such changes are not uncommon in healthcare services and they will have to adapt to the new situation by increasing the number of services remotely offered. Limited mobility has resulted in interruption of treatments that traditionally have been administered through face-to-face modalities, especially those related to cognitive impairments. In this telerehabilitation approach, both the patient and the specialist physician enter a virtual reality (VR) environment where they can interact in real time through avatars. A spaced retrieval (SR) task is implemented in the system to analyze cognitive performance. An experimental group (n = 20) performed the SR task in telerehabilitation mode, whereas a control group (n = 20) performed the SR task through a traditional face-to-face mode. The obtained results showed that it is possible to carry out cognitive rehabilitation processes through a telerehabilitation modality in conjunction with VR. The cost-effectiveness of the system will also contribute to making healthcare systems more efficient, overcoming both geographical and temporal limitations.