Human-centered User Interfaces for Automated Driving – (Un-)exploited Potentials

Designing user interfaces for (highly) automated driving is a complex task since users vary considerably regarding their needs and preferences. Therefore, a one-size-fits-all approach will not be sufficient for designing these interfaces. Thus, in this paper we aim to identify unexploited potentials in this area. We do so by performing a systematic literature review. Our contributions are 1) a systematization of human-centered user interface design for automated driving in four key aspects, 2) the research intensity per aspect, 3) the unexploited potential within each aspect and 4) the potentials of the relations between them. Concretely, current research lacks frameworks supporting the customization of the named interfaces based on user characteristics. Among others, personalization of displayed information shows unexploited potentials for acceptance and usability. Thus, we recommend future research to focus on human-centricity accounting for individual needs instead of the interface itself

AI-Based Adaptive Learning: A Systematic Mapping of the Literature

With the aid of technology advancement, the field of education has seen a noticeable transformation. The teaching-learning process is now more interactive and is no longer restricted to students' physical presence in the classroom but instead makes use of specialized online platforms. In recent years, solutions that offer learning routes customized to learners' needs have become more necessary. In this regard, artificial intelligence has served as an excellent answer, allowing for the building of educational systems that can accommodate a wide range of student needs. Through this paper, a systematic mapping of the literature on AI-based adaptive learning is presented. The examination of 93 articles published between 2000 and 2022 made it possible to draw several conclusions, including the number of adaptive learning environments based on AI, the types of AI algorithms used, the objectives targeted by these systems as well as factors related to adaptation. This study may serve as a springboard for further investigation into how to address the problems raised by the current state.&nbsp

Towards a threat assessment framework for consumer health wearables

The collection of health data such as physical activity, consumption and physiological data through the use of consumer health wearables via fitness trackers are very beneficial for the promotion of physical wellness. However, consumer health wearables and their associated applications are known to have privacy and security concerns that can potentially make the collected personal health data vulnerable to hackers. These concerns are attributed to security theoretical frameworks not sufficiently addressing the entirety of privacy and security concerns relating to the diverse technological ecosystem of consumer health wearables. The objective of this research was therefore to develop a threat assessment framework that can be used to guide the detection of vulnerabilities which affect consumer health wearables and their associated applications. To meet this objective, the Design Science Research methodology was used to develop the desired artefact (Consumer Health Wearable Threat Assessment Framework). The framework is comprised of fourteen vulnerabilities classified according to Authentication, Authorization, Availability, Confidentiality, Non-Repudiation and Integrity. Through developing the artefact, the threat assessment framework was demonstrated on two fitness trackers and their associated applications. It was discovered, that the framework was able to identify how these vulnerabilities affected, these two test cases based on the classification categories of the framework. The framework was also evaluated by four security experts who assessed the quality, utility and efficacy of the framework. Experts, supported the use of the framework as a relevant and comprehensive framework to guide the detection of vulnerabilities towards consumer health wearables and their associated applications. The implication of this research study is that the framework can be used by developers to better identify the vulnerabilities of consumer health wearables and their associated applications. This will assist in creating a more securer environment for the storage and use of health data by consumer health wearables