Adaptive Augmented Reality Model: Local Context with Storytelling Adaptation in Heritage

Adaptive Augmented Reality responds to the user’s characteristics, interests and context with useful and effective real-time information. Users' needs are crucial in enhancing their interaction experience. Currently the emerging technology allows such better support. However, one of the problems identified is the lack of a formal definition of a model required by such technology to adapt to local context and environment. Furthermore, storytelling as a mechanism to enhance users' experience while interacting in such augmented reality space is rarely included. Therefore, the main aim of this research is to propose a formal definition of such model in the forms of user, context, interaction and environment models. These models will then be implemented in an archaeology field as a proof of concept. The main aim of this research is tp propose a formal definition of AAR model in the forms of user, context, interaction and environment models

Research review on augmented reality as an educational resource for people with intellectual disabilities

The present study reviewed nine publications related to the application of augmented reality (AR)-based interventions to people with intellectual disabilities (ID). The lines of research developed focus on the areas of education, personal autonomy and health promotion. The studies are theoretical frameworks, design of AR-based applications, or quasi-experimental study designs without a control group. The samples, all of less than 15 subjects, have been chosen under intentional criteria. The devices that seem to prevail as preferred for its application in this group are the portable ones due to the wide range of intervention possibilities that they offer as well as the development potential of the personal autonomy that they allow. Results offer a positive view of the application of this technology to people with ID, although the characteristics of the studies do not allow yet its generalization. Among the reported benefits are: enhancing learning achievement, motivation and enjoyment of tasks, helping to understand information, enhancing orientation, raising the level of engagement

Інтеграція технологій доповненої та віртуальної реальності з адаптивними системами навчання: аналіз концептуальних моделей

Augmented reality (AR) and virtual reality (VR) are increasingly utilized in education to provide interactive and engaging learning experiences. However, most applications do not fully exploit the potential of AR/VR technologies for adaptive and personalized learning. This paper analyzes five recent conceptual models that integrate adaptive techniques into AR/VR educational systems to identify their core components and capabilities. All reviewed models incorporate a user profile, content repository, interaction data, environment representation, and device components. Detailed user information is collected, including demographics, knowledge levels, cognitive characteristics, sensory-motor abilities, and emotional-motivational factors. This enables adapting AR/VR content to individual learners' needs, styles, and states. Two key adaptation-influencing components were identified across the models - the environment and the user adaptation mechanism based on the user model. Additional components depend on the service level and specifics of the device. For mobile applications, cloud computing enables optimal processing of objects, location, and human data. The analysis determined these models provide a strong conceptual basis for adaptive AR/VR learning systems. However, further research is needed to develop a universal framework considering domain specifics. An ontological approach should be employed to allow customization for particular educational contexts. This could significantly enhance the state of adaptive AR/VR learning systems. Existing conceptual models incorporate promising techniques but lack holistic frameworks tailored to educational domains. Developing such frameworks is essential to advance research and practice in adaptive AR/VR learning. The analysis and findings presented provide a foundation to guide future efforts in designing and evaluating adaptive AR/VR educational systems.Технології доповненої (AR) та віртуальної (VR) реальності все частіше використовуються в освіті для створення інтерактивного та захоплюючого досвіду навчання. Проте, більшість додатків не використовують повною мірою потенціал технологій AR/VR для адаптивного та персоналізованого навчання. Ця стаття аналізує п'ять нещодавно запропонованих концептуальних моделей, які інтегрують адаптивні методики в освітні системи AR/VR, щоб визначити їх основні компоненти та можливості. Всі проаналізовані моделі включають профіль користувача, сховище контенту, дані про взаємодію, представлення середовища та компоненти пристроїв. Збирається детальна інформація про користувача, включаючи демографічні дані, рівень знань, когнітивні характеристики, сенсорно-моторні здібності та емоційно-мотиваційні чинники. Це дозволяє адаптувати контент AR/VR до індивідуальних потреб, стилів та станів здобувачів вищої освіти. У всіх моделях було визначено два ключові компоненти впливу на адаптацію - середовище та механізм адаптації користувача на основі моделі користувача. Додаткові компоненти залежать від рівня обслуговування та особливостей пристрою. Для мобільних додатків хмарні обчислення дозволяють оптимізувати обробку даних про об'єкти, місце розташування та людину. У дослідженні визначено, що ці моделі забезпечують ґрунтовну концептуальну основу для адаптивних систем навчання AR/VR. Проте, необхідні подальші дослідження для розробки універсальної структури з урахуванням специфіки предметної області. Слід застосовувати онтологічний підхід, щоб дозволити адаптацію для конкретних освітніх контекстів. Це могло б значно покращити стан адаптивних систем навчання AR/VR. Існуючі концептуальні моделі включають багатообіцяючі методики, але не мають цілісних структур, адаптованих до освітніх галузей. Розробка таких структур є важливою для просування досліджень та практики адаптивного навчання AR/VR. Представлений аналіз та результати закладають фундамент для майбутніх досліджень у проектуванні та оцінці адаптивних освітніх систем AR/VR

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Technology in Rehabilitative Interventions for Children

This Special Issue is aimed to offer an overview of studies presenting new rehabilitation approaches addressed to children with neurodevelopmental disorders, designed to enhance the effects of learning processes through the use of new technologies. The contributions of this Special Issue, authored by researchers and clinicians from some of the most valued Italian scientific institutions in the field of neurodevelopmental disorders, can offer some useful data and advice on the use of technology in rehabilitation and telerehabilitation to researchers, rehabilitators, clinicians and pratictioners (psychologists, neuropsychologists, speech therapists, etc.)

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Dynamic assessment of e-learning in foreign languages programs in sustainable and emergency digitization formats

The global pandemic and subsequent quarantine measures and restrictions have posed an array of challenges to the structure and procedure of higher education workflow, which influenced significantly the scope of individual experiences, projected outcomes and estimated quality of higher education in countries across the world. The subsequent warfare in Ukraine has brought forth emergency digitization measures called to adapt and adjust digital learning formats to the technological, emotional and educational challenges of the active warzone. This study focus is the in-depth assessment of the progress in individual digital and hybrid learning experiences by students of different tiers (Bachelor’s level, Master’s level, Graduate school level) in Oriental (Mandarin Chinese, Japanese) and European (French, Italian, Spanish, English, German) Languages university level programs at Borys Grinchenko Kyiv University of Ukraine through the span of educational activities in the time-frame of COVID-19 quarantine measures (sustainable digitization) of 2020-2021 and the timespan of active warfare (emergency digitization measures) of 2022. The comparative survey benchmarking and analysis of different e-learning dimensions is used to assess the progress and challenges of individual quality and efficiency of translation of the real life Foreign Languages Acquisition practices into digital and hybrid format, involving activation of interoperable skills and cross-sectorial activities, facilitated by digital tools