Artificial Intelligence (AI) and related technologies have been employed to simulate human decision-making processes to improve people's lives. Accordingly, AI knowledge and related competence are crucial, especially for students pursuing information technology (IT) and computer-related degrees, since they will eventually be the next generation of AI designers or users. While the importance of AI technology and its applications have been widely discussed and explored, AI technology in Vietnam is in a nascent stage due to the shortage of skilled experts and the reluctance to adopt AI applications by businesses. Thus, the primary objective of this study was to explore the knowledge of and competence in AI among Vietnamese university IT students. A total of 206 university students from software engineering and computer science programs participated in this study. The results indicate the need for a focused effort to establish a strong foundation for a comprehensive and accessible AI mandatory course plan for IT students.

Shelf space is a scarce and expensive resource in the retail industry because a large number of products compete for limited display space. Thus, shelf-space allocation is frequently implemented in shops to increase product sales and profits. In the past few decades, numerous models and solution methods have been developed to deal with the shelf-space allocation problem (SSAP). In this paper, a novel population-oriented metaheuristic algorithm, teaching–learning-based optimization (TLBO) is applied to solve the problem and compared with existing solution methods with respect to their solution performance. Further, a hybrid algorithm that combines TLBO with variable neighborhood search (VNS) is proposed to enhance the performance of the basic TLBO. The research results show that the proposed TLBO-VNS algorithm is superior to other algorithms in terms of solution performance, in addition to using fewer control parameters. Therefore, the proposed TLBO-VNS algorithm has considerable potential in solving SSAP.

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