Purpose: This article explores the adoption of Artificial Intelligence (AI) in Human Resource Management (HRM) in the UAE, focusing on the critical challenges of fairness, bias, and privacy in recruitment processes. The study aims to understand how AI is transforming HR practices in the UAE, highlighting the issues of bias and privacy while examining real-world applications of AI in recruitment, employee engagement, talent management, and learning and development. Methodology: Through case study methodology, detailed insights are gathered from these companies to understand real-world applications of AI in HRM. A comparative analysis is conducted, comparing AI-driven HRM practices in UAE-based organizations with international examples to highlight global trends and best practices. Findings: The research reveals that while AI holds significant potential to streamline HR functions such as recruitment, onboarding, performance monitoring, and talent management, it also discusses challenges and strategies companies face and develop in integrating AI into their HRM processes, reflecting the broader context of AI adoption in the UAE’s HR landscape. Originality: This paper contributes to the growing body of literature on AI in HRM by focusing on the unique context of the UAE, a rapidly developing market with a highly diverse workforce. It highlights the specific challenges and opportunities faced by organizations in the UAE when implementing AI in HRM, particularly regarding fairness, bias, and data privacy.

The concept of output-oriented education has been introduced for many years in our country and has been widely used in the process of personnel training in Chinese universities. This paper discusses how the concept of Outcome Based Education can be fully integrated into the process of developing talents in an interdisciplinary and collaborative manner in the context of new engineering. We have made useful explorations in various aspects from curriculum system integration, online teaching resources construction, studio-style course organization mode, rich teaching project library to school-enterprise cooperation project practice, etc., which have improved students' learning effect.

At this stage, network technology is developing rapidly. The resources in the network are massive, and a large number of resources are distributed in a decentralized and heterogeneous manner. With the continuous expansion of the application scope of distributed technology, it can provide effective scheme guidance for resource application. Combined with the current situation of network teaching platform and relevant functional requirements, it is very necessary to apply distributed technology. Taking DFS technology as an example, this paper studies the shared resource management scheme of this technology in network storage, and studies the specific application effect and path of DFS technology in distributed network teaching platform.