  With the advance of artificial intelligence (AI), the emergence of Google
Gemini and OpenAI Q* marks the direction towards artificial general
intelligence (AGI). To implement AGI, the concept of interactive AI (IAI) has
been introduced, which can interactively understand and respond not only to
human user input but also to dynamic system and network conditions. In this
article, we explore an integration and enhancement of IAI in networking. We
first comprehensively review recent developments and future perspectives of AI
and then introduce the technology and components of IAI. We then explore the
integration of IAI into the next-generation networks, focusing on how implicit
and explicit interactions can enhance network functionality, improve user
experience, and promote efficient network management. Subsequently, we propose
an IAI-enabled network management and optimization framework, which consists of
environment, perception, action, and brain units. We also design 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. We demonstrate the effectiveness of the framework through case
studies. Finally, we discuss potential research directions for IAI-based
networks.
