Sustainable knowledge work increasingly depends on hybrid human and artificial intelligence ecosystems that combine scale with judgment, tacit know-how, and relational intelligence (Jarrahi et al., 2023; Mosqueira-Rey et al., 2023). Reviews of human-in-the-loop approaches have identified practical design patterns that enhance robustness and usability when teams explicitly specify human roles and workflow handoffs (Mosqueira-Rey et al., 2023; Amershi et al., 2014). Research in knowledge management indicates that natural language processing and structured elicitation facilitate the externalization of tacit expertise; however, these methods often lose context and nuance without facilitation (Zaoui Seghroucheni et al., 2025; Taherdoost & Madanchian, 2023). Empirical studies warn that offloading judgment can erode domain skills unless organizational design preserves learning opportunities and reflective practice (Sambasivan & Veeraraghavan, 2022; Crowston & Bolici, 2024). Research also documents trade-offs between productivity gains and worker wellbeing; poorly governed artificial intelligence can raise cognitive load, surveillance concerns, and job stress (Valtonen et al., 2025; Organization for Economic Co-operation and Development, 2025). The literature, therefore, calls for socio-technical solutions that integrate explainability, provenance, and human-centered workflow design with human resource strategies for reskilling and psychological safety (Adadi & Berrada, 2018; National Institute of Standards and Technology, 2021). This track advances those solutions and asks how to measure hybrid outcomes that balance performance, cognitive load, and well-being (Jarrahi et al., 2023).

Objectives

• Surface empirical and theoretical advances about human-AI knowledge co-creation.
• Share HR strategies that sustain employee creativity, psychological safety, and skill development in AI-augmented roles.
• Investigate methods for capturing and transferring tacit knowledge in hybrid systems.

Scope/example topics

• Human-in-the-loop KM systems and design patterns
• Augmenting tacit knowledge: coaching, mentorship, and AI tools
• Workforce reskilling, micro-learning, and competence mapping for hybrid work
• Human-AI workflow design, handoffs, and boundary objects
• Measurement: hybrid knowledge metrics, cognitive load, and productivity vs well-being trade-offs
• Ethical issues: deskilling, algorithmic bias in knowledge recommendations, consent, and provenance of derived knowledge