Immersive technologies are rapidly transforming the field of education. Amongst them, Augmented Reality (AR) has shown promise as a resource, particularly for education in Science, Technology, Engineering, Arts, and Mathematics (STEAM). There are, however, few teachers deploying this new medium in the classroom directly, and, consequently, only a few, elect students benefit from the AR-enriched offers. Curricula are already overloaded, and schools generally lack developmental resources, thus leaving no room for experimentation. This situation is further aggravated by the too few educational applications available with sufficient learning content. In this article, we investigate the method of Active Learning for the teaching of STEAM subjects, using a format where students are tasked with building an AR application as part of their learning. We evaluate the applicability of the Active Learning for STEAM subjects with a qualitative, case study approach, applying the workshop format as an extracurricular activity in our work with students from a range of secondary schools in Oxford. We discuss how the format works, so it can be embedded into regular curricula, not just as an extracurricular activity, also providing an overview on the involved teaching units and rationale. All teams in our preview audience of the case study succeeded in building working applications, several of impressive complexity. Students found that the lessons were enjoyable and AR technology can enhance their learning experience. The Active Learning method served as a catalyst for students’ skills development, with the case study providing evidence of learning to code, working with a physics simulation engine, ray-tracing, and geometry, learning how to manage teams and interact with other students/instructors, and engineering a working prototype of a game. We consequentially argue that combining the STEM subjects and the arts, using the proposed Active Learning format, is able to provide a more holistic and engaging education.

Industry 4.0 is a coordinated push for automation in Smart Factories and other Cyber-Physical Systems (CPS). The increasing complexity of frequently changing production environments challenges shop floor workers to perform well. The tasks they work on are getting less routine and ask for continuous knowledge and skills development. For example, the skills portfolio of workers likely requires improved higher-order thinking and decision-making skills. A wide range of research and development efforts already today sets focus on different areas of workplace learning, including performance appraisals, pedagogy and education, technology, and business economics. Bridging the skills gap, however, requires novel user-facing technologies -- such as Augmented Reality (AR) and wearables -- for human performance augmentation to improve efficiency and effectiveness of staff delivered through live guidance. AR branches out beyond mobile apps with 3D-object superimposition for marketing purposes to rather complex use cases delivered by a rapidly growing innovation ecosystem of hard- and software providers collaborating closely with R&D organisations. This paper provides a first shared vision on how AR can tackle four different challenges related to handling complexity in a CPS environment: develop intelligent assistance systems for learning and performance assessment at the workplace, adapt job profiles accordingly, and last but not least to address also the issue of work-life balance. The paper concludes with an outline of a research roadmap.

Background: Cognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non‐invasive biometric sensors, unbiased real‐time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject fields, and reported results are fragmented.Objectives: The aim of this review is to analyse systematically how physiological methods have been used to measure cognitive load and what the implications are for the future research on AR‐based tools.Methods: This paper took the systematic review approach. Through screening with 10 exclusion criteria, 23 studies, that contain 3 key elements: AR‐based intervention, cognitive state examination and physiological methods, were identified, analysed and synthesised.Results: Physiological methods in their current form require reference to provide meaningful interpretations and suggestions. Therefore, they are often combined with conventional methods. Many studies investigate the effect of wearable devices in comparison with non‐AR stimuli, which has been controversial, but detection of different causes of cognitive load are on the horizon. Eye‐tracking is the method most used and most consistent in the use of its parameters.Conclusions: A multi‐method approach combining two or more evaluation instruments is essential for the validation of users' cognitive state. In addition to the AR stimuli in question, having another independent variable such as task difficulty in experiment design is useful. Statistical approaches with more data input could help establish a reliable scale. The future research should attempt to dissociate cognitive load caused by different effects such as device, instruction, and other AR techniques as well as intrinsic and extraneous aspects, in a better experimental setup with multiple parameters.

Since its debut in the field of education nearly three decades ago, Artificial Intelligence (AI) has been considered as a powerful tool to facilitate new paradigms for instructional design and innovative educational practice in the form of intelligent tutoring systems, chatbots, teaching robots and adaptive learning systems among others. Recent technological advances in the adjacent areas of natural language processing, machine learning and computer graphics focusing primarily on design features that can improve their human-like qualities of naturalness and believability as human interlocutors have also amplified new application opportunities for Intelligent Virtual Agents (IVAs) or Animated Pedagogical Agents (APAs) within the area of Intelligent Computer-Assisted Language Learning (ICALL). Although AI-powered IVAs hold the potential to enhance the learning process in nearly any knowledge domain and personalize automation in teaching by embodying different roles in the learning environment, strikingly few studies have empirically attempted to assess IVAs impact on L2 learners’ academic achievement when learning English as a Foreign (EFL) so far. This study addresses this issue via a systematic review of relevant interventionist IVA studies that were conducted in EFL settings and published within the 2015-2020 timeframe examining IVAs key affordances, major barriers in their adoption for language learning purposes, and the CALL research trends currently prevalent on the topic. Pedagogical implications for the effective implementation of IVA technology in L2 contexts are discussed and future research avenues in the area are highlighted.

With the recent developments in augmented reality (AR) technologies comes an increased interest in the use of smart glasses for hands-on training. Whether this interest is turned into market success depends at the least on whether the interaction with smart AR glasses satisfies users, an aspect of AR use that so far has received little attention. With this contribution, we seek to change this. The objective of the article, therefore, is to investigate user satisfaction in AR applied to three cases of practical use. User satisfaction of AR can be broken down into satisfaction with the interaction and satisfaction with the delivery device. A total of 142 participants from three different industrial sectors contributed to this study, namely, aeronautics, medicine, and astronautics. In our analysis, we investigated the influence of different factors, such as age, gender, level of education, level of Internet knowledge, and the roles of the participants in the different sectors. Even though users were not familiar with the smart glasses, results show that general computer knowledge has a positive effect on user satisfaction. Further analysis using two-factor interactions showed that there is no significant interaction between the different factors and user satisfaction. The results of the study affirm that the questionnaires developed for user satisfaction of smart glasses and the AR application performed well, but leave room for improvement.