In response to the increasing youth unemployment rate and the demand for future-oriented career development, university student entrepreneurship has emerged as a critical domain in both economic policy and education. This study conducts a comprehensive literature review to examine the interrelationships between entrepreneurship, entrepreneurship education, entrepreneurial competency, and entrepreneurial intention among university students, with an emphasis on the Human Resource Development (HRD) perspective. The review reveals that entrepreneurial mindset significantly influences students’ intention to start a business, while entrepreneurship education contributes both directly and indirectly through the development of entrepreneurial competencies. Entrepreneurial competencies serve as a practical foundation for translating intention into action and are integral to HRD’s goal of competency-based talent development. The study further highlights that entrepreneurship education aligned with HRD principles—such as experiential learning, self-directed development, and learning organization frameworks—can foster employability and self-employment capacity. This integrative analysis suggests that university entrepreneurship programs should not be seen merely as policy instruments, but rather as strategic HRD initiatives for developing future-ready, opportunity-creating human capital. Implications for educational design, policy development, and future empirical research are discussed.

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.