In a world marked by relentless change, the landscape of work, knowledge sharing, and innovation is undergoing a transformation of unprecedented proportions. Knowledge has long been recognized as a critical asset, fuelling organizational competitiveness, fostering the development of intellectual capital, and catalysing enhanced performance, product innovation, and creativity (Mesmer-Magnus & DeChurch, 2009). Traditionally, knowledge transfer and exchange were perceived through a unidimensional lens, characterized by the transfer of insights from a sharer to a recipient. However, this perspective has evolved into a multidimensional paradigm, emphasizing interpersonal interactions and now, the integration of machines, particularly artificial intelligence (AI), into the knowledge sharing equation (Chen et al., 2014).
The adoption of AI applications has ushered in a new era of knowledge sharing, one facilitated by AI-mediated knowledge sharing (AI-MKS) social exchange (Gursoy et al., 2019; Malik et al., 2022). This evolution is timely for several reasons. First, the interactions between humans and AI applications are on the rise, creating a pressing need to understand the implications and intricacies of this burgeoning relationship. Second, our comprehension of the issues arising from these interactions remains limited, demanding deeper exploration. Lastly, while there is increasing interest in AI-MKS, research on the considerations for adopting such applications, their drivers, benefits, and potential problems remains sparse.
AI-MKS social exchange offers a multitude of advantages over traditional human-to-human knowledge exchange. Firstly, it grants unparalleled flexibility, enabling users to share knowledge without constraints of time and space (Nguyen & Malik, 2022). This is particularly valuable for multinational corporations and large organizations, where employees may have limited face-to-face interactions or are geographically dispersed (Nguyen & Malik, 2022). However, despite these merits, existing literature predominantly focuses on AI-MKS acceptance, leaving gaps in understanding other critical aspects, such as employee motivation within this AI-mediated social exchange, especially when considering human resource management (HRM)-focused AI applications.
It is worth noting that the intersection of AI, HRM, and knowledge sharing has garnered limited research attention thus far, despite its growing significance (Malik et al., 2021). We provide below a list of topics relevant to the track, but topics not included in the list below may also be considered:

• How does the integration of AI technologies impact the traditional knowledge sharing processes within organizations?
• What are the key drivers and challenges of AI adoption in knowledge sharing, and how do they vary across different industries and organizational sizes?
• What role does AI play in enhancing knowledge discovery and retrieval, and how does this influence innovation and decision-making in organizations?
• How can organizations effectively manage the human-AI collaboration in knowledge sharing? What are the best practices for achieving a harmonious balance between AI and human involvement?
• What ethical considerations should organizations take into account when implementing AI-mediated knowledge sharing systems, and how can they ensure responsible AI use in knowledge exchange?
• In what ways can AI-powered knowledge sharing platforms contribute to cross-border collaboration and knowledge integration, particularly in multinational organizations?
• How does the adoption of AI-mediated knowledge sharing affect employee engagement, job satisfaction, and overall workplace dynamics?
• What strategies can organizations employ to motivate employees to actively participate in AI-mediated knowledge sharing and mitigate potential resistance or reluctance?
• What are the long-term implications of AI-mediated knowledge sharing for workforce skill development and career growth?
• How can HRM practices be tailored to support the effective implementation and management of AI in knowledge sharing, and what challenges might HR professionals face in this context?
• What are the potential risks associated with AI-mediated knowledge sharing, such as information security and privacy concerns, and how can these risks be mitigated effectively?
• How does the quality of AI algorithms and natural language processing impact the accuracy and relevance of knowledge shared through AI systems, and how can these technologies be improved for better knowledge exchange outcomes?

These research questions aim to explore the multifaceted dimensions of AI-mediated knowledge sharing in the context of the future of work, addressing both its opportunities and challenges. Researchers can use these questions as a starting point to delve deeper into this evolving field and contribute valuable insights to the conference track.

Ashish Malik, Queen’s University of Belfast, Northern Ireland
Mai Nguyen, Griffith University, Australia