  The transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 brings
new requirements and opportunities to current sensing approaches, especially in
light of recent progress in Chatbots and Large Language Models (LLMs).
Therefore, the advancement of parallel intelligence-powered Crowdsensing
Intelligence (CSI) is witnessed, which is currently advancing towards
linguistic intelligence. In this paper, we propose a novel sensing paradigm,
namely conversational crowdsensing, for Industry 5.0. It can alleviate workload
and professional requirements of individuals and promote the organization and
operation of diverse workforce, thereby facilitating faster response and wider
popularization of crowdsensing systems. Specifically, we design the
architecture of conversational crowdsensing to effectively organize three types
of participants (biological, robotic, and digital) from diverse communities.
Through three levels of effective conversation (i.e., inter-human, human-AI,
and inter-AI), complex interactions and service functionalities of different
workers can be achieved to accomplish various tasks across three sensing phases
(i.e., requesting, scheduling, and executing). Moreover, we explore the
foundational technologies for realizing conversational crowdsensing,
encompassing LLM-based multi-agent systems, scenarios engineering and
conversational human-AI cooperation. Finally, we present potential industrial
applications of conversational crowdsensing and discuss its implications. We
envision that conversations in natural language will become the primary
communication channel during crowdsensing process, enabling richer information
exchange and cooperative problem-solving among humans, robots, and AI.
