Learning from a Generative AI Predecessor -- The Many Motivations for
  Interacting with Conversational Agents
Donald Brinkman Jonathan Grudin
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.