This paper explores the integration of interactive AI (IAI) into next-generation networking. IAI is a concept that enables AI systems to interactively understand and respond to both human user input and dynamic system and network conditions. The paper first reviews recent developments and future perspectives of AI, then introduces the technology and components of IAI. It then explores the integration of IAI into next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. The paper proposes an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. It also designs the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. The paper demonstrates the effectiveness of the framework through case studies. Finally, it discusses potential research directions for IAI-based networks. IAI is a type of AI that emphasizes immediate and direct interaction between AI and users, unlike human-in-the-loop (HITL) systems that rely on human input. IAI systems can instantaneously understand user inputs, such as voice commands, text messages, or other interactive commands, and intelligently respond or execute tasks based on these inputs. This ability enhances user experience and increases the flexibility and effectiveness of AI applications in dynamic environments. Integrating IAI with technologies such as retrieval-augmented generation (RAG) and LangChain can further personalize network operations. RAG enables IAI systems to extract information from vast databases to enrich their responses and decisions. Combined with LangChain, which extends AI reasoning capabilities, IAI can provide more context-aware solutions based on the existing databases. The main advantages of IAI over HITL systems, especially when augmented with RAG and LangChain, include customizability and personalizability, better flexibility, and less bias. IAI is particularly important to integrate into wireless networks due to its dynamic nature and requirement to continuously adapt to dynamic changes. The capabilities of IAI are particularly promising in addressing these challenges. Its interactive adaptive resource management can optimize the utilization of network resources and improve network performance. For example, in a network with changing user requirements, IAI can dynamically allocate bandwidth to maintain high performance given instantaneous user experience feedback. The paper proposes an IAI-enabled problem formulation framework, which consists of the following units: Perception, Brain, Action, and Environment. The Perception component draws from diverse sources and modalities, enabling the IAI to assimilate information from text, visuals, and numerical data. The Brain is the central component of the IAI system, functioning in a three-unit structure: database, storage, and decision-making. The RAG database contains a wealth of searchable academic texts, such as those pertaining to unmanned aerial vehicles (UAVs), reconfigurable intelligent surfaces (RISs), wireless power transfer (WPT), and more. TheThis paper explores the integration of interactive AI (IAI) into next-generation networking. IAI is a concept that enables AI systems to interactively understand and respond to both human user input and dynamic system and network conditions. The paper first reviews recent developments and future perspectives of AI, then introduces the technology and components of IAI. It then explores the integration of IAI into next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. The paper proposes an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. It also designs the pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. The paper demonstrates the effectiveness of the framework through case studies. Finally, it discusses potential research directions for IAI-based networks. IAI is a type of AI that emphasizes immediate and direct interaction between AI and users, unlike human-in-the-loop (HITL) systems that rely on human input. IAI systems can instantaneously understand user inputs, such as voice commands, text messages, or other interactive commands, and intelligently respond or execute tasks based on these inputs. This ability enhances user experience and increases the flexibility and effectiveness of AI applications in dynamic environments. Integrating IAI with technologies such as retrieval-augmented generation (RAG) and LangChain can further personalize network operations. RAG enables IAI systems to extract information from vast databases to enrich their responses and decisions. Combined with LangChain, which extends AI reasoning capabilities, IAI can provide more context-aware solutions based on the existing databases. The main advantages of IAI over HITL systems, especially when augmented with RAG and LangChain, include customizability and personalizability, better flexibility, and less bias. IAI is particularly important to integrate into wireless networks due to its dynamic nature and requirement to continuously adapt to dynamic changes. The capabilities of IAI are particularly promising in addressing these challenges. Its interactive adaptive resource management can optimize the utilization of network resources and improve network performance. For example, in a network with changing user requirements, IAI can dynamically allocate bandwidth to maintain high performance given instantaneous user experience feedback. The paper proposes an IAI-enabled problem formulation framework, which consists of the following units: Perception, Brain, Action, and Environment. The Perception component draws from diverse sources and modalities, enabling the IAI to assimilate information from text, visuals, and numerical data. The Brain is the central component of the IAI system, functioning in a three-unit structure: database, storage, and decision-making. The RAG database contains a wealth of searchable academic texts, such as those pertaining to unmanned aerial vehicles (UAVs), reconfigurable intelligent surfaces (RISs), wireless power transfer (WPT), and more. The