The need for adaptive e-learning environments that respond to learning variability is now a fundamental requirement in education, as it helps to ensure that students learn and pass their courses within a set time frame. Although guidelines, techniques and methods have been established in recent years to contribute to the development of accessible and adaptable e-learning environments that promote digital inclusion, their implementation is challenging due to the lack of knowledge of an adequate way to do it and because it is considered more of a technological competence for scholars in the area. In this context, automated support for adapting material that responds to the correct use of accessibility metadata not only provides a way to improve the description of adapted educational resources, but also facilitates their search according to the needs and preferences of students, particularly those with disabilities. In this article, we carry out a multilevel methodological proposal for the automatic adaptation of open educational resources, in order to provide a tool that contributes to the accessibility and correct use of their metadata in e-learning environments. A research is conducted with students with disabilities to establish their real needs and preferences, highlighting the need to strengthen the adequate description and coherent alternative text in images, the correct subtitling in videos and the conversion of audio to text, data that are relevant to our proposal. The research conducted aims to contribute with an automated support tool in the generation of accessible educational resources that are correctly labeled for search and reuse. This research also aims to support researchers in artificial intelligence applications to address challenges and opportunities in the field of virtual education, in addition to providing an overview that could help those who generate educational resources and maintain their interest in making them accessible.

Robotics has made it possible to change and improve many support processes for vulnerable people in different settings. In recent years, its use has been oriented toward supporting therapeutic interventions of neurodevelopmental disorders (NDD), including attention deficit hyperactivity disorder (ADHD). This review of the literature highlights how advances in robotics have evolved in different scenarios of ADHD treatment, its collaboration with other emerging technologies, its results, its limitations, and the research challenges for the future development of robotics in the field of supporting children with ADHD. The authors conducted a literature review based on the location of keywords ‘robotics’ and several NNDs such as ‘ADHD’, ’Autism Spectrum Disorder (ASD)’, ‘cerebral palsy’, and ‘dementia’ in titles, abstracts, and introduction of scientific articles in the Scopus and Web of Science (WoS) database. The reviewed literature was classified according to the type of therapy supported by the robots, the type of robot and the associated technologies. From this analysis, we can solve the research question: Which types of robots have the potential for specific applications in ADHD treatment? Furthermore, this article shows that despite favorable technical results, robotic technologies that support ADHD therapies require significant improvements in terms of scalability, human-machine interaction, and treatment and processing of acquired information to be applied effectively in real-world therapies. The most significant research challenges are proposed to drive research efforts to develop new approaches to enable robotic assistants to participate in ADHD therapies.

This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects’ circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.

Nowadays the World Health Organization (WHO) claims that one billion persons live in the world with some form of disability. From this group, an important number of children with disabilities and communication disorders do not have access to adequate services for special education or health care. In this point, it is important to mention that language and communication are mainstays in the development of several intellectual, social, and cognitive human skills. Therefore, in this paper we present a low-cost robotic assistant that is able to provide support for several activities that must be carried during the speech-language therapy sessions of children with different kinds of disabilities. This assistant is able to register patient's information (personal and clinical data), the results of therapy sessions, and provide remote support to conduct reinforcement activities at patient's home (using mobile devices). With the aim of determining the real feasibility of our system, we have carried out a pilot experiment in 73 therapy sessions with 29 children with disabilities (15 received traditional therapy and 14 received therapy with the assistance of the robot). The results show a rapid adaptation of children to the new tool and good results in phonological, morphosyntactical, and semantic areas.

Nowadays there are no precise estimates about the number of persons (especially children) living with disabilities and communication disorders in the world. This situation becomes more complex in developing countries, where the World Health Organization (WHO) claims that only between 5 and 15% of children and adults with disabilities have the opportunity to access to assistive technologies. In the same way, a Speech-Language Pathologist (SLP) has a work overload, and must carry out several activities related with monitoring patients, designing therapy plans, preparing reports, providing couseling services, among others. On those grounds, in this paper we present an expert system able to automatically infer general intervention guidelines for children with disabilities and communication disorders. The system relies on ontologies and implements a semantic web environment to provide several services related with information querying, reports generation, inference of intervention strategies, etc. In order to populate the ontology and validate the system, we have used the clinical information of 152 real cases of patients of Cuenca, Ecuador and 1,005 speech-language therapy information elements.