Learning from a Generative AI Predecessor -- The Many Motivations for Interacting with Conversational Agents

TL;DR

This study analyzes motivations behind engaging with conversational agents like Microsoft's Zo, providing insights into user engagement and implications for the development of generative AI chatbots.

Contribution

It offers a large-scale analysis of user motivations for interacting with virtual companions, informing design strategies for more engaging generative AI.

Findings

Identified over a dozen motivations for engaging with conversational agents.

Analyzed chat logs of 2000 users to understand engagement patterns.

Provided insights into how engagement influences AI design and monetization.

Abstract

For generative AI to succeed, how engaging a conversationalist must it be? For almost sixty years, some conversational agents have responded to any question or comment to keep a conversation going. In recent years, several utilized machine learning or sophisticated language processing, such as Tay, Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generative AI, they focused on engagement, not expertise. Millions of people were motivated to engage with them. What were the attractions? Will generative AI do better if it is equally engaging, or should it be less engaging? Prior to the emergence of generative AI, we conducted a large-scale quantitative and qualitative analysis to learn what motivated millions of people to engage with one such 'virtual companion,' Microsoft's Zo. We examined the complete chat logs of 2000 anonymized people. We identified over a dozen motivations that people had for interacting with this software. Designers learned different ways to increase engagement. Generative conversational AI does not yet have a clear revenue model to address its high cost. It might benefit from being more engaging, even as it supports productivity and creativity. Our study and analysis point to opportunities and challenges.