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Draft:Conversational AI

In today's world, Draft:Conversational AI is a topic that has gained great relevance and has captured the attention of different sectors. From academia to the business world, Draft:Conversational AI has become a topic of constant discussion and growing interest. Over time, Draft:Conversational AI has proven to have a significant impact on society, generating debates and reflections that transcend borders and cultures. In this article, we will explore the phenomenon of Draft:Conversational AI in depth, analyzing its implications and influence on today's world.

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  • Comment: Chatbots communicating using speech rather than solely text is mentioned in the main chatbot article, adding a sentence to the beginning doesn't suggest this should have an independent article. -Samoht27 (talk) 21:07, 21 May 2025 (UTC)
  • Comment: I don't think this warrants it's own topic separate from chatbot, but I think maybe its information could be used on the main article. -Samoht27 (talk) 20:20, 21 May 2025 (UTC)

Conversational AI is a subfield of artificial intelligence that enables computers and machines to communicate with people using natural language. It includes a range of systems that support naturalistic dialogue, spanning multiple modalities — such as voice, text, gesture, and visual interfaces — and is used in both consumer and public-facing contexts.

Although often associated with chatbots, conversational AI encompasses a broader set of technologies and applications. These include Draft:Voice-First_AI systems like public intercoms and smart speakers, multimodal kiosks that combine speech and display, and infrastructure-level tools used in transportation, accessibility, and healthcare. While chatbots are typically confined to text-based interfaces in customer service or web platforms, conversational AI also covers voice agents, spoken dialogue systems, and real-time physical interfaces.

Overview

Conversational AI encompasses a range of technologies that allow machines to understand, process, and respond to human language. It includes text-based chatbots, voice-first systems like smart speakers and AI-enabled intercoms, and multimodal platforms that combine speech, visuals, and gesture.[1][2]

These systems are employed in domains such as customer service, healthcare, education, and transportation. They rely on components like automatic speech recognition (ASR), natural language understanding (NLU), dialog management, and text-to-speech (TTS) synthesis.[3]

History

The roots of conversational AI trace back to early programs like ELIZA (1966), which simulated a Rogerian psychotherapist.[4] Later systems like PARRY and SHRDLU expanded on this with more sophisticated rule-based conversations.[5]

Modalities and Interfaces

Text-Based Systems

Text-based conversational agents, or chatbots, appear on websites and messaging platforms. Early versions were rule-based; newer ones use machine learning.[6][7]

Voice-First AI

Voice-first systems use speech as the primary interface, such as intercoms, phone bots, and smart speakers. They are optimized to function in noisy environments and across diverse accents.[8] (See: Voice-First AI)

Multimodal Interfaces

Multimodal systems combine speech, text, visuals, and gestures. These are used in kiosks, AR/VR platforms, and accessibility devices.

Technology Stack

  • ASR: Converts speech to text
  • NLU: Interprets user intent
  • Dialog Management: Manages conversation logic
  • TTS: Synthesizes voice responses
  • Backends: Connect to APIs, CRMs, or databases[9]

Applications

  • In customer service, AI chat/voice agents reduce support volume
  • In healthcare, virtual assistants help with triage and clinical documentation
  • In transportation, AI-enabled help points and intercoms assist travelers with directions, schedules, and safety information
  • In education, tutoring bots support students with feedback and Q&A
  • In accessibility, voice-first AI enhances usability for people with impairments

Design and Usability

Designers of conversational AI consider user intent, turn-taking, latency, and cultural nuance. Public-facing systems also prioritize privacy, transparency, and trust.[10]

Challenges and Criticism

  • Misunderstanding or misinterpreting user input
  • Biases in training data[11]
  • Voice data privacy and consent concerns[12]
  • Language bias and limited multilingual support[13]
  • Overreliance on automation for sensitive interactions

See Also

References

  1. ^ "Multimodal Conversational AI". Retrieved May 21, 2025.
  2. ^ "Multimodal Conversation Design". 7 September 2021. Retrieved May 21, 2025.
  3. ^ "How Conversational AI Works". Retrieved May 21, 2025.
  4. ^ "The History of Chatbots". 15 February 2024. Retrieved May 21, 2025.
  5. ^ "History of Artificial Intelligence". Retrieved May 21, 2025.
  6. ^ "Building a Rule-Based Chatbot". 25 June 2024. Retrieved May 21, 2025.
  7. ^ "Types of Chatbots". IBM. 7 March 2025. Retrieved May 21, 2025.
  8. ^ "The Rise of Voice-First Customer Interactions". Retrieved May 21, 2025.
  9. ^ "How Conversational AI Works". Retrieved May 21, 2025.
  10. ^ "Privacy Concerns and Solutions". 8 June 2024. Retrieved May 21, 2025.
  11. ^ "Conversational AI Challenges". Retrieved May 21, 2025.
  12. ^ "AI Chatbots and Privacy" (PDF). Retrieved May 21, 2025.
  13. ^ "AI's Language Gap". 8 September 2023. Retrieved May 21, 2025.
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