Different areas of education have undergone a significant transformation due to the rapid progress of extended reality in recent years. Nonetheless, existing eXtended Reality (XR) authoring tools often require advanced programming skills, limiting their accessibility to educators and students. To address this challenge, we introduce BabloXR, an intuitive authoring tool that leverages Blockly to enable visual, code-free programming for creating educational XR environments. BabloXR was evaluated through a user study involving teachers and students who successfully designed their own XR learning environments. The results indicate that users of BabloXR were able to create complete and detailed scenarios, with usability testing revealing high ease of use, positive user experience, and strong interest in extended reality.

The growth of social networks as mass media has enabled faster and closer interaction between users, but it also presents challenges, such as the risk of spreading hate speech. Early detection of such harmful posts is critical. This article presents a methodology to create a unique corpus of Spanish-language comments collected from MisProfesores.com platform, covering all states in Mexico. This process resulted in a dataset of 18,000 unlabeled samples and 853 manually labeled samples. In addition to describing the corpus construction process, the results of the evaluation of different models trained with these data are presented, as well as their comparison with previous works for toxicity detection, highlighting the relevance of the Spanish corpus development for specific tasks. As a result, our Transformer-based model performed better than the state-of-the-art models in the binary toxicity classification, reaching a value of 0.9649 in accuracy and 0.9645 in F1 score.

The field of natural language processing (NLP) is one of the first to be addressed since artificial intelligence emerged. NLP has made remarkable advances in recent years thanks to the development of new machine learning techniques, particularly novel deep learning methods such as LSTM networks and transformers. This chapter presents an overview of how deep learning techniques have been applied to NLP in the area of affective computing. The chapter examines traditional and novel deep learning architectures developed for natural language processing (NLP) tasks. These architectures comprise recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and the cutting-edge transformers. Moreover, a methodology for NLP method training and fine-tuning is presented. The chapter also integrates Python code that demonstrates two NLP case studies specializing in the educational domain for text classification and sentiment analysis. In both cases, the transformer-based machine learning model (BERT) produced the best results. Request access from your librarian to read this chapter's full text.