Table 3.

Characterization of AI-powered chatbots [19].

Dialogue management	Finite-state	Users are guided through a predetermined series of steps or states in the dialogue.
	Frame-state	Users are presented with inquiries to fill in placeholders within a template, allowing for task-specific execution based on their input.
	Agent-based	Agent-based systems facilitate advanced communication between multiple agents, enabling them to reason about their own actions and beliefs while considering others’. The dialogue model in these systems adapts dynamically, incorporating contextual information at each interaction step.
Dialogue initiative	User	Users assume an active role in guiding and directing the ongoing conversation.
	System	The conversation is steered and directed by the system, with the system taking the lead in guiding the interaction.
	Mixed both	Both the user and the system have the capacity to initiate and guide the conversation, assuming the role of conversation leaders interchangeably.
Input modality	Spoken	Users interact with the system using spoken language.
	Written	Users engage with the system through written language for interaction purposes.
Output modality	Spoken, written and visual modes of communication encompass various channels of expression, including verbal interaction, written text and visual cues such as facial expressions and body movements.
Task-oriented	Yes	The system is designed for a specific task, engaging in concise conversations to acquire essential information and accomplish objectives, like scheduling consultations.
	No	The system’s objective is not solely focused on quickly achieving a predetermined outcome like casual chatbots do.