---
id: conversational-ai-systems
title: "Conversational AI: Task-Oriented Dialogue and Open-Domain Chatbots"
schema_type: article
category: ai
language: en
confidence: medium
last_verified: "2026-05-28"
created_date: "2026-05-24"
generation_method: ai_structured
ai_models:
  - claude-4.5-sonnet
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
completeness: 0.85
atomic_facts:
  - id: af-ai-conversational-ai-systems-1
    statement: >-
      A Neural Conversational Model applies sequence-to-sequence learning to conversational response
      generation.
    source_title: A Neural Conversational Model
    source_url: https://arxiv.org/abs/1506.05869
    confidence: medium
  - id: af-ai-conversational-ai-systems-2
    statement: The BlenderBot work combines large-scale training recipes for open-domain chatbots.
    source_title: Recipes for building an open-domain chatbot
    source_url: https://arxiv.org/abs/2004.13637
    confidence: medium
  - id: af-ai-conversational-ai-systems-3
    statement: >-
      LaMDA focuses on language models trained for dialog applications and evaluates qualities such
      as sensibleness and specificity.
    source_title: "LaMDA: Language Models for Dialog Applications"
    source_url: https://arxiv.org/abs/2201.08239
    confidence: medium
primary_sources:
  - id: ps-ai-conversational-ai-systems-1
    title: A Neural Conversational Model
    type: academic_paper
    year: 2015
    institution: arXiv
    url: https://arxiv.org/abs/1506.05869
  - id: ps-ai-conversational-ai-systems-2
    title: Recipes for building an open-domain chatbot
    type: academic_paper
    year: 2020
    institution: arXiv
    url: https://arxiv.org/abs/2004.13637
  - id: ps-ai-conversational-ai-systems-3
    title: "LaMDA: Language Models for Dialog Applications"
    type: academic_paper
    year: 2022
    institution: arXiv
    url: https://arxiv.org/abs/2201.08239
known_gaps:
  - Handling ambiguity and clarification in dialogue
  - Emotion-aware conversational systems
disputed_statements: []
secondary_sources: []
updated: "2026-05-28"
---
## TL;DR
Conversational AI: Task-Oriented Dialogue and Open-Domain Chatbots: Conversational AI systems generate or select responses in dialogue settings such as chatbots, assistants, and customer support tools.

## Core Explanation
The field spans early rule-based systems, neural dialogue models, retrieval, safety policies, grounding, and evaluation. Modern dialogue systems need fluency, task success, factuality, moderation, and user trust.

## Further Reading

- [A Neural Conversational Model](https://arxiv.org/abs/1506.05869)
- [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637)
- [LaMDA: Language Models for Dialog Applications](https://arxiv.org/abs/2201.08239)