The Role of AI In Vocational Education: A Systematic Literature Review

This research aims to comprehensively investigate and explain the important role of Artificial Intelligence (AI) in the field of education, with a particular focus on vocational education. Using the systematic literature review (SLR) method as the chosen research approach, the researchers carefully scrutinized a selection of articles published in the time span between 2018 to 2023, which were carefully selected from the Scopus database. The PRISMA method, renowned for its precision, was carefully applied to screen and filter out documents that did not fit the strict inclusion and exclusion criteria outlined for the study. Upon examination and review of the selected articles, an interesting pattern emerged, underscoring the considerable impact and transformative potential of AI in the education sector, with a particular emphasis on vocational education. The advent of AI technologies has ushered in a new era, one that is brimming with potential to revolutionize and enhance the teaching-learning experience in educational settings. It is quite clear that a thoughtful and strategic implementation of AI promises to improve the efficiency, personalization, and overall effectiveness of education, thus paving the way for a brighter and more adaptive educational landscape

From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques

Mobile Sensing Apps have been widely used as a practical approach to collect behavioral and health-related information from individuals and provide timely intervention to promote health and well-beings, such as mental health and chronic cares. As the objectives of mobile sensing could be either \emph{(a) personalized medicine for individuals} or \emph{(b) public health for populations}, in this work we review the design of these mobile sensing apps, and propose to categorize the design of these apps/systems in two paradigms -- \emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both sensing paradigms might incorporate with common ubiquitous sensing technologies, such as wearable sensors, mobility monitoring, mobile data offloading, and/or cloud-based data analytics to collect and process sensing data from individuals, we present a novel taxonomy system with two major components that can specify and classify apps/systems from aspects of the life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \& Participation}, \emph{(2) Health Surveillance \& Data Collection}, and \emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different goals of the two paradigms, this work systematically reviews this field, and summarizes the design of typical apps/systems in the view of the configurations and interactions between these two components. In addition to summarization, the proposed taxonomy system also helps figure out the potential directions of mobile sensing for health from both personalized medicines and population health perspectives.Comment: Submitted to a journal for revie