Migratable AI : Investigating users' affect on identity and information migration of a conversational AI agent

TL;DR

This study examines how users' emotional responses are affected by different configurations of information and identity migration in conversational AI agents, revealing key emotional patterns linked to migration scenarios.

Contribution

It provides empirical insights into users' affective responses to various migration parameters in conversational AI, highlighting the emotional impact of identity and information transfer.

Findings

Users felt most joy and surprise when both information and identity were migrated.

Users experienced most anger when only information was migrated without identity.

The affective responses vary significantly based on migration configurations.

Abstract

Conversational AI agents are becoming ubiquitous and provide assistance to us in our everyday activities. In recent years, researchers have explored the migration of these agents across different embodiments in order to maintain the continuity of the task and improve user experience. In this paper, we investigate user's affective responses in different configurations of the migration parameters. We present a 2x2 between-subjects study in a task-based scenario using information migration and identity migration as parameters. We outline the affect processing pipeline from the video footage collected during the study and report user's responses in each condition. Our results show that users reported highest joy and were most surprised when both the information and identity was migrated; and reported most anger when the information was migrated without the identity of their agent.