This paper presents a study applying Large Language Models and Augmented Reality to Second Language Acquisition, focusing on spoken communication. Literature and expert insights show growing interest in vocabulary training, but limited focus on other language skills. We developed a prototype extending an existing educational Augmented Reality platform by integrating an original Retrieval-Augmented Generation model to support spoken communication. A randomized controlled trial with Norwegian learners and domain experts evaluated the system. Results show increased engagement, while the learning outcomes were comparable. Experts rated the original Retrieval-Augmented Generation model as superior to foundational language models.

While stereoscopic 3D visualization is reported to have positive impacts on the delivery of visuospatial information, little is known about its cognitive mechanism. This study investigates the cognitive load incurred during mental rotation tasks with eye-tracking, comparing the stereoscopic 3D stimuli with 2D equivalent both presented by Microsoft HoloLens2. The study also explores the separability of different cognitive aspects using multiple fixation metrics: total fixation time, average fixation duration, and fixation frequency. While all three parameters differentiated easy/difficult questions, the difference between correctly/incorrectly answered questions was only seen in total fixation time and dispersion-based fixation frequency, and the difference between 2D/3D questions was only observed in average fixation duration and velocity-based fixation frequency. Results suggest that 3D representations could be perceived as more detailed helping users create better mental images for visuospatial operations, and that these observed eye-tracking parameters reflect different cognitive aspects.

Novel eXtended Reality (XR) learning activities are being introduced, planned, and explored in teaching at many, if not most Higher Education institutions. However, there are widespread concerns regarding how these innovations can be used in ways that are accessible and inclusive. A lot of questions are being posed, and only parts of the answers are available. Within this contribution, we review opportunities and challenges associated with XR learning in the more narrow field of Online and Distance Learning (ODL). ODL is the EdTech sector which, through its challenges of 24/7 and global availability, often pioneers technologies ready for the mainstream. For this, we first provide an overview of accessibility needs in a diverse student population, focusing on disabled students), to then review the legal and regulatory context which frame the obligations of institutions. We reflect on what already exists and on potential contributions to the development of XR and its use by all. We introduce three heterogeneous examples to illustrate the use of XR learning in ODL, to then describe how legal and regulatory obligations translate to the institutional context. We discuss gaps and open problems, and outline future work possible.

BackgroundCognitive 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.ObjectivesThe 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.MethodsThis 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.ResultsPhysiological 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.ConclusionsA 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. What is already known about this topicImplications for practiceThe efficacy of AR-based instructional tools can be evaluated using the concept of cognitive load.Cognitive load has been conventionally measured by self-reporting and performance.There is a growing interest in physiological measurement of cognitive load with non-invasive biometric sensors.What this paper addsThis paper systematically analyses how physiological methods are used to measure cognitive load in AR.It synthesises the fragmented results of AR studies conducted with a range of physiological methods.Combining two or more evaluation instruments is essential for the validation of users' cognitive state.Eye-tracking is the method most used, and fixation duration is the most consistent parameter.A statistical approach with more biometric data input could help establish a reliable cognitive load scale.Future research should attempt to separate cognitive load caused by different effects and different cognitive aspects.

The use of Augmented Reality (AR) holds significant promise for language learning. AR enriches the learning experience by overlaying virtual elements onto the real environment, offering versatile resources for language learning. Various AR technology platforms, including headsets, glasses and mobile devices can be used to enhance language competencies such as listening, speaking, reading, writing, and linguistic components like grammar and vocabulary. The most commonly reported benefits of AR are increased learner motivation, engagement, enjoyment, reduced anxiety, elevated confidence levels and enhanced cultural awareness. In addition, well-designed AR resources may help develop 21st-century skills such as critical thinking, collaboration and creativity. Despite the rapid development of AR technology and the growth of its adoption, a literature gap exists in terms of practical guidelines for designing and implementing AR activities in language learning classrooms. This paper addresses this gap by showcasing AR activities for different languages, proficiency levels, and educational stages while providing practical guidelines for designing and implementing AR activities in language learning classrooms.

Augmented reality (AR) is rapidly emerging as an increasingly useful technology in educational settings. In the ARETE (Augmented Reality Interactive Educational System) H2020 project, consortium members designed and implemented an ecosystem aimed at supporting teachers in building a collaborative learning environment through the use of AR in order to improve educational experiences. In particular, one of the pilot projects aims to introduce AR into school behavior lessons for the first time, leveraging the Positive Behaviour Intervention and Support (PBIS) methodology. Specifically, in this paper we will discuss the proposed architecture within the ARETE project that incorporates AR technology into the learning process of behavior lessons to support the teaching, practice and reinforcement phases of expected behaviors. Through the combination of different technologies and systems, it is possible to create an example of a technological and innovative ecosystem designed for creating behavioral lessons in AR.

Augmented Reality in education can support students in a wide range of cognitive tasks–fostering understanding, remembering, applying, analysing, evaluating, and creating learning-relevant information more easily. It can help keep up engagement, and it can render learning more fun. Within the framework of a multi-year investigation encompassing primary and secondary schools across Europe, the ARETE project developed several Augmented Reality applications, providing tools for user interaction and data collection in the education sector. The project developed innovative AR learning technology and methodology, validating these in four comprehensive pilot studies, in total involving more than 2,900 students and teachers. Each pilot made use of a different Augmented Reality application covering specific subjects (English literacy skills, Mathematics and Geography, Positive Behaviour, plus, additionally, an Augmented Reality authoring tool applied in a wide range of subjects). In this paper, we introduce the datasets collected during the pilots, describe how the data enabled the validation of the technology, and how the approach chosen could enhance existing augmented reality applications in data exploration and modelling.

The field of Technology Enhanced Learning (TEL) in recent years has drawn much attention and has been widely researched. Research has mainly focused on the theory, implementation in various disciplines and modes, and the role of TEL, especially in Higher Education. Researchers, however, are called upon to focus on the interdisciplinarity of TEL as well as on its potential in supporting doctoral students, one level above the usual research carried out on undergraduate courses. This paper aims at presenting part of the quantitative results yielded from an online survey which collected information on doctoral education in TEL from PhD candidates and researchers involved in doctoral education or carrying out research in TEL. The findings have shown that despite the increase in the availability of resources and materials as doctoral students move to more advanced stages of their studies, they need adequate support and training in academic writing and research methodologies. This has prompted the design and implementation of a training program for doctoral students by the Doctoral Education for Technology-Enhanced Learning project (DE-TEL), which aimed to improve and innovate the European doctoral education in TEL, by developing a new program and Open Education Resources in this field.

Students are likely to receive feedback on their writing from several different academics during their undergraduate studies. Sometimes this feedback is partial, only mentioning one or two highlights and issues: ignoring the bulk of what was written. Sometimes feedback can be inconsistent between academics and confuse students. Even where there is a very clear rubric for marking a piece of academic writing, different colleagues may give significantly different marks; and the mark a single academic gives might depend on the time it was given. This variation reveals an opportunity to better support students in learning to produce good quality academic text.In this paper we describe a new online tool designed to give students automated feedback on their academic writing. Students’ work is held confidentially and feedback is provided without any interaction from their tutor. It forms a zero stakes transaction. The resulting Open Essay Optimiser (OEO) is a tuned and enhanced successor of an earlier tool called Open Essayist. It was tuned using many anonymised scripts from students and enhanced with the input of expert programmers and designers. OEO examines the argument coherence within a text: how ideas are set out, discussed and then brought together in a conclusion. It provides different ways to present this coherence to students, whilst allowing them to edit their text within the tool and see the impact of these changes. There are other features including the listing and highlighting key words and phrases, and key sentences. Also, a review of the references used and the possibility to suggest others. This paper will include the outcomes of the initial trial, including the student response to it, and consider its potential to support students.

Humanoid intelligent agents, or ‘Holographic AIs’, as we prefer, are trending, promising improved delivery of personalized services on smart glasses and in Augmented Reality. Lacking clarity of the concept and missing recommendations for their features, however, pose a challenge to developers of these novel, embodied agents. In this paper, we therefore conduct a comparative analysis of nine intelligent agents who can interact with both physical and virtual surroundings. We identify, select, and investigate four distinct types of nonplayer game characters, chatbot agents, simulation agents, and intelligent tutors in order to, subsequently, develop a framework of features and affordances for holographic AIs along the axes of appearance, behavior, intelligence, and responsiveness. Through our analysis, we derive preliminary recommendations for developers of Holographic AIs: the use case determines appearance; dialogue management is key; awareness and adaptation are equally important for successful personalization; and environmental responsiveness to events both in the virtual and digital ream is needed for a seamless experience.

Trust is an essential attitude in social relationships, but it also mediates our approach to certain technology. The definition of interpersonal trust, however, is too wide to expound our understanding of how trust impedes such interaction with technology, and the lack of an applicable quantifiable model in particular presents an obstacle to our quest of building reliable, trusted, and intelligent holographic agents. In this paper, we therefore develop a novel metric scale to measure trust. We identify, select, and refine over a hundred items related to trust, check their precision and validity with the help of a judges panel, and select polarising items that are able to bring out the distinctive characteristics regarding people’s trust towards intelligent agents. Our findings indicate that an assessment of trust involves looking at the user’s belief about the agent’s competence, integrity, benevolence, and compassion, which drive the attitude of trust, influenced by the user’s general propensity to trust. Trust then drives intention to engage and ultimately engagement, which, if successful, results in the establishment of a trust relationship with the agent. While we propose an item-response scale for measuring this model of trust, we also add our deliberations on how elements of it could be replaced with alternative means that possibly offer more immediacy than self-inspection, discussing in particular alternatives for measuring elements of compassion, competence, and social relationships.

The amount of texts available on the web is growing continuously and making sense of this unstructured data efficiently and effectively, therefore, poses a demanding challenge for organizations. Although computer science community has developed many techniques, there is ample room for improvement on organizational utilization of such text data, especially when referring to decision-making support. In this article, we propose and validate a framework towards an effective use of text data inside hotel industry, bringing tourism sector to this discussion. We combined three text mining techniques for text classification, sentiment analysis and topic modeling in a novelty way to allows managers to analyze guests' comments and compares competitors in hospitality industry based on SERVQUAL. Our objective is to present an automatized process involving text data collection and analysis, improving decision-making process.

The integration of augmented reality (AR) technology into personal computing is happening fast, and augmented workplaces for professionals in areas such as Industry 4.0 or digital health can reasonably be expected to form liminal zones that push the boundary of what currently possible. The application potential in the creative industries, however, is vast and can target broad audiences, so with UNBODY, we set out to push boundaries of a different kind and depart from the graphic-centric worlds of AR to explore textual and aural dimensions of an extended reality, in which words haunt and re-create our physical selves. UNBODY is an AR installation for smart glasses that embeds poetry in the user’s surroundings. The augmented experience turns reality into a medium where holographic texts and film clips spill from dayglow billboards and totems. In this paper, we develop a blueprint for an AR escape room dedicated to the spoken and written word, with its open source code facilitating uptake by others into existing or new AR escape rooms. We outline the user-centered process of designing, building, and evaluating UNBODY. More specifically, we deployed a system usability scale (SUS) and a spatial interaction evaluation (SPINE) in order to validate its wider applicability. In this paper, we also describe the composition and concept of the experience, identifying several components (trigger posters, posters with video overlay, word dropper totem, floating object gallery, and a user trail visualization) as part of our first version before evaluation. UNBODY provides a sense of situational awareness and immersivity from inside an escape room. The recorded average mean for the SUS was 59.7, slightly under the recommended 68 average but still above ‘OK’ in the zone of low marginal acceptable. The findings for the SPINE were moderately positive, with the highest scores for output modalities and navigation support. This indicated that the proposed components and escape room concept work. Based on these results, we improved the experience, adding, among others, an interactive word composer component. We conclude that a poetry escape room is possible, outline our co-creation process, and deliver an open source technical framework as a blueprint for adding enhanced support for the spoken and written word to existing or coming AR escape room experiences. In an outlook, we discuss additional insight on timing, alignment, and the right level of personalization.

Mixed Reality (MR) technologies, including augmented and virtual reality, are increasingly used in a number of sectors, thanks to their capabilities to immerse users in multisensory interaction environments.However, as indicated by some systematic reviews, questionnaire remains the main method for evaluating the interaction quality of MR.There is a lack of innovative approaches addressing unique features of MR.It can dampen the advances of MR, as evaluation feedback can inform its future development.In this workshop we aim to explore this issue by inviting participants to share their practical experiences or conceptual ideas of evaluating MR in various contexts, using different methods and tools.The ultimate aim is to produce a research agenda on this topic for the community to examine it further in the future.Mixed reality.

Once the World Health Organization declared the COVID-19 outbreak a pandemic, many countries abruptly established a lock down requiring their populations to stay home to avoid any contact with others to stop the spread of the disease Consequently, most schools and higher education institutions closed access to campuses and face-to-face class meetings were suspended Students were sent home and temporarily left without access to traditional educational resources The migratory solution for this situation is moving toward extensive use of distance learning tools and techniques However, many teachers were not prepared for this transition There remains a gap in knowledge about how to quickly transform educational content and manage e-teaching In this paper, we describe the process of transforming a face-to-face course in Augmented Reality to the online format in a rapid way We wish to establish case evidence for educators regarding how to convert traditional course content to online content, in the face of incidents, such as the COVID-19 pandemic In this paper, we present an approach including examples and highlighting opportunities for educators in higher education to support the transformation of courses for distance learning © 2020 for this paper by its authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0)

This paper outlines the objectives of the working group on developing a model Augmented Reality curriculum for higher education. We motivate the need for the model curriculum by the growing Augmented Reality industry and subsequent demand for trained professionals. While the industry is growing, the educational offers that train the required skills remain limited and fragmented. The working group will address this challenge by surveying the state of the art in Augmented Reality education are reviewing available data on industry requirements. Based on the results, the group will develop a new model Augmented Reality curriculum. The working group will also develop future work recommendations for the design of teaching materials and integration of Augmented Reality in computing curricula.

Augmented Reality has found its way into training scenarios, e.g., when used in companies to deliver close-to-practice instruction. As a prerequisite for wide-scale adoption, however, there is need for a structured approach to model and formalize training activity. For this reason, the Augmented Reality IEEE standard ARLEM was defined. The standard was developed with the help of a reference implementation, MIRAGE XR, to demonstrate how real-life training applications can be created and edited using an Augmented Reality editor for learning experiences. With IEEE ARLEM and Mirage XR, standardized and interoperable learning content can be created and exchanged.

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.

Technology enhanced learning (TEL) research connects Learning Sciences, Educational Psychology, and Computer Science, in order to investigate interventions based on digital technologies in education and training settings. In this paper, we argue that doctoral training activity for TEL needs to be situated at the intersection of disciplines in order to facilitate innovation. For this, we first review the state of disciplinarity in TEL, reviewing existing meta-studies of the field. Then, we survey 35 doctoral education programs in Europe in which doctoral students working on TEL topics are enrolled. Findings indicate that most doctoral schools are associated with a single discipline and offer methodological rather than content-specific modules. TEL-specific content is provided only in exceptional cases, creating a potentially isolating gap between master-level education and scientific conferences. On this background, we argue that cross-institutional doctoral training is important to progress TEL as a field. In this article, we study and share the approach of an international doctoral summer school organized by the European society EA-TEL over the past 15 years. The summer school provides foundational methodological knowledge from multiple disciplines, content-specific topical knowledge in TEL, access to cutting edge scientific discourse, and discussion of horizontal issues to doctoral students. We further provide an analysis of shifting program topics over time. Our analysis of both, institutional as well as cross-institutional doctoral training in TEL, constitutes this paper’s core contribution in that it highlights that further integration of perspectives and knowledge is to be done in TEL; together with codification and explication of knowledge in the intersection of disciplines.