This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

Since the Industrial Revolution, there has been an evolution in the paradigms under which the industrial worker is perceived and dealt with. These paradigms can be briefly listed in the order of their evolutionary stage as: the food-gatherer, the economic man, the social man, the resourceful man, and the enterprising man. Each of them is a combination of two basic paradigms in different proportions, namely, the outsider paradigm and the partnership paradigm. Obviously, the paradigmatic perspectives of management about their workers will have a significant influence on how they treat their workers, which may become especially conspicuous during recessions and other kinds of hard times. It was in this context that we designed a study to understand the human resource strategies of companies during a period of recession. Data for this study was collected through the content analysis of 46 published cases, wherein we developed the ratings of two sets of variables, namely: the external and internal environments of the company and the strategic actions taken by the respective managements. A surprising finding of the study is that the correlations between the environmental factors and the strategy factors were small and non-significant; moreover, the correlations involving the external environment were smaller than those involving the internal environment. Hence, it may be inferred that strategic actions are influenced primarily by the paradigmatic perspectives of management rather than environmental factors. In order to identify the different types of paradigmatic perspectives, we have further carried out a cluster analysis to develop a taxonomy of paradigms. The results showed that there are five sub-paradigms, which are: (1) Pacifiers, constituting 35% of the sample; (2) Modifiers, constituting 22%; (3) Molders, constituting 17%; (4) Enhancers, constituting 15%; and (5) Exploiters, constituting 11%. The limitations of the study and the implications of the findings are discussed in the concluding part.

Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.