This article presents the results of a survey on the use of digital avatars and agents in museums and places of cultural interest. The optimization of virtual agents in the cultural heritage domain is an interdisciplinary undertaking and this paper investigates pertinent research and solutions and suggests ways forward. The research questions examined relate to (a) the technological characteristics of cultural heritage-related uses of users’ avatars and virtual agents, and patterns that emerge, and (b) suggestions for future research based on this article’s findings. We reviewed relevant publications and analysed the approaches presented to identify trends and issues that could lead to conclusions on the existing state of the field and, moreover, infer and suggest future directions. The main findings relate to a trend toward onsite, sophisticated installations or applications with increasing investment in mixed reality. Moreover, emphasis shifts toward optimising agents such as virtual guides or companions, mediators of cultural content and engaging facilitators. Behavioural Realism (BR), featured mostly in virtual reality installations, greatly fosters engagement according to the reviewed research, and we conclude that mixed reality onsite applications, which are gathering pace, should reach their degree of sophistication and combine the strengths of both MR and BR.

Recently, the use of Electroencephalography (EEG) in scientific research on User Authentication (UA) has led to cutting-edge experiments that seek to identify and authenticate individuals based on their brain activity in particular usage scenarios. Utilizing EEG signals, derived from brain activity, might provide innovative solutions to contemporary security issues in traditional knowledge-based user authentication, including the threat of shoulder surfing. In this review paper, we analyze 108 different EEG-based user authentication experiments based on the following perspectives: a) the user experimental setup, with an emphasis on the applied EEG- protocols; b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad time frame from 1998 to 2022 and include various experimental protocols and algorithms used for classifying EEG signals. Additionally, the majority of the referenced works report findings from multiple experiments that incorporate distinct approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.

Preprocessing electroencephalographic (EEG) signals during computer-mediated Cognitive Load tasks is crucial in Human-Computer Interaction (HCI). This process significantly influences subsequent EEG analysis and the efficacy of Artificial Intelligence (AI) models employed in Cognitive Load Assessment. Consequently, it stands as an indispensable procedure for developing dependable systems capable of adapting to users’ cognitive capacities and constraints. We systematically analyzed fifty-seven (57) research papers on computer-mediated Cognitive Load EEG experiments published between 2018 and 2023. The preprocessing methods identified were multiple, controversial, and strongly dependent on the particularities of each experiment and the derived experimental dataset. Our investigation involved the meticulous classification of preprocessing methods based on distinct parameters, namely the degree of user intervention, the noise level, and the subject pool size. Particular attention was paid to semi-automated denoising technology since conventional methods, advanced approaches, and standardized pipelines overwhelm research, but no optimum solution is available yet. This survey is anticipated to provide a valuable contribution to the rising demand for an efficient and fully automated preprocessing approach in EEG-based computerized Cognitive Load experiments.

The credibility of online examinations in Higher Education is hardened by numerous factors and use-case scenarios. This paper reports on a longitudinal study, that spanned over eighteen months, in which various stakeholders from three European Higher Education Institutions (HEIs) participated, aiming to identify core threat scenarios experienced during online examinations, and to, accordingly, propose threat models, data metrics and countermeasure features that HEI learning management systems can embrace to address the identified threat scenarios. We also report on a feasibility study of an open-source intelligent and continuous student identity management system, namely TRUSTID, which implements the identified data metrics and countermeasures. A user evaluation with HEI students (n = 133) revealed that the TRUSTID system is resilient and effective against impersonation attacks, based on intelligent face and voice identification mechanisms, and scored well in usability and user experience. Aspects concerning the preservation of privacy in storing, retrieving and processing sensitive personal data are also discussed.

Mixed Reality (MR) technologies have transformed the way in which we interact and engage with digital content, offering immersive experiences that blend the physical and virtual worlds. Over the past years, there has been increasing interest in employing Artificial Intelligence (AI) technologies to improve user experience and trustworthiness in cultural contexts. However, the integration of Large Language Models (LLMs) into MR applications within the Cultural Heritage (CH) domain is relatively underexplored. In this work, we present an investigation into the integration of LLMs within MR environments, focusing on the context of virtual art exhibitions. We implemented a HoloLens MR application, which enables users to explore artworks while interacting with an LLM through voice. To evaluate the user experience and perceived trustworthiness of individuals engaging with an LLM-based virtual art guide, we adopted a between-subject study design, in which participants were randomly assigned to either the LLM-based version or a control group using conventional interaction methods. The LLM-based version allows users to pose inquiries about the artwork displayed, ranging from details about the creator to information about the artwork’s origin and historical significance. This paper presents the technical aspects of integrating LLMs within MR applications and evaluates the user experience and perceived trustworthiness of this approach in enhancing the exploration of virtual art exhibitions. Results of an initial evaluation provide evidence about the positive aspect of integrating LLMs in MR applications. Findings of this work contribute to the advancement of MR technologies for the development of future interactive personalized art experiences.