Over the past year, defined by the COVID-19 pandemic, we have witnessed a boom in applying key emerging technologies in education. In such challenging situations, technology and education expanded their work together to strengthen and interactively impact the learning process in the online higher education context. From a pedagogical perspective, extended reality (XR) and artificial intelligence (AI) were accessible toolboxes to amplify an active and learner-centered teaching method. Whether and how such activities will continue in a post-COVID-19 situation remains unclear. In this systematic literature review, we document the application of XR and AI in online higher education settings and build up an accurate depiction of their influence after the COVID-19 pandemic outbreak. A significant contribution of the thorough analysis conducted was the corroboration of the growing interest of these fast-emerging technologies and their impact on learner agency and outcomes, making online education more accessible, effective, engaging, collaborative, self-paced, and adapted to the diverse academic trajectories. The momentum brought about by the pandemic has served as an impulse for educators and universities to expand the use of these technologies progressively, meet new challenges, and shape the future of online higher education.

Technological revolutionary changes have boosted mobile learning’s evolution from supplementary material for teaching to a flexible, strategic, and convenient resource, driving new paths in higher education. With global increases in wireless internet access and the advent of highly functional smartphones and tablets, which have impacted the rise in mobile device ownership, mobile learning has expanded its applications as a direct way to implement tailored learning settings. Notably, during the COVID-19 pandemic, together with other educational technologies, it became a solicited tool in remote education. In this systematic review, we will explore how educators and researchers have been documenting the development and impact of mobile learning tools in the teaching and learning process since the COVID-19 outbreak. Results show that, embedded with online higher education programs, mobile learning has empowered interaction in content creation, communication, and collaboration between learners and instructors, significantly impacting learning effectiveness. Moreover, although this technology is well established in higher education, it remains attractive for educators who actively use it because of its pedagogic potential.

Cranial bone thickness varies among modern humans, and many factors influencing this variability remain unclear. Growth hormones and physical activity are thought to influence the vault thickness. Considering that both systemic factors and energy supply influence the vascular system, and taking into account the structural and biomechanical interaction between endocranial vessels and vault bones, in this study we evaluate the correlation between vascular and bone diameters. In particular, we tested the relationship between the thickness of the parietal bone (which is characterized, in modern humans, by a complex vascular network) and the lumen size of the middle meningeal and diploic vessels, in adult modern humans. Our results show no patent correlation between the thickness of parietal bone and the size of the main vascular channels. Values and distributions of the branching patterns, as well as anatomical relationships between vessels and bones, are also described in order to provide information concerning the arrangement of the endocranial vascular morphology. This information is relevant in both evolutionary and medical contexts. Anat Rec, 299:888–896, 2016.

Synopsis: Artificial intelligence (AI) is poised to revolutionize many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitized specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long-standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorizing them into 3 stages based on their order of appearance: (1) machine learning, (2) deep learning, and (3) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilized and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments has the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of “big data” that aligns the study of phenotypes with genomics and other areas of bioinformatics.