Social intelligence systems often face two fundamental limitations, data sparsity and model generality, when the systems need to adapt to new domains or scenarios where training data is limited or unavailable. This is particularly important in critical social intelligence applications such as health truth discovery and disaster damage assessment. This chapter introduces two human-AI hybrid approaches, CrowdAdapt and CollabGeneral, that explore human intelligence and domain expertise to effectively bridge the knowledge gap between data-rich source domains and emergent target domains. Two fundamental challenges exist in developing such hybrid approaches, including: (1) the domain discrepancy where knowledge transfer is degraded by irrelevant or inapplicable information from source domains, and (2) the optimal trade-off between model generality and domain specificity that ensures a social intelligence model can perform well across different domains while maintaining high accuracy for domain-specific characteristics and patterns. Two real-world case studies demonstrate the superiority and great potential of CrowdAdapt and CollabGeneral in addressing these challenges and advancing the development of adaptive social intelligence systems.