Migratable AI: Effect of identity and information migration on users perception of conversational AI agents

TL;DR

This study investigates how migrating user identity and information across different forms of conversational AI affects user perceptions, finding that combined migration enhances trust, competence, likability, and social presence.

Contribution

It introduces a Migratable AI system and empirically examines the effects of identity and information migration on user perceptions across multiple device forms.

Findings

Identity migration improves trust, competence, social presence.

Information migration enhances trust, competence, and likability.

Combined migration yields the highest user perception scores.

Abstract

Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and environments to always accompany and assist its user to support a far more continuous, personalized, and collaborative experience. This opens the question of what properties of a conversational AI agent migrates across forms, and how it would impact user perception. To explore this, we developed a Migratable AI system where a user's information and/or the agent's identity can be preserved as it migrates across form factors to help its user with a task. We designed a 2x2 between-subjects study to explore the effects of information migration and identity migration on user perceptions of trust, competence, likeability, and social presence. Our results suggest that identity migration had a positive effect on trust, competence, and social presence, while information migration had a positive effect on trust, competence, and likeability. Overall, users report the highest trust, competence, likeability, and social presence towards the conversational agent when both identity and information were migrated across embodiments.