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

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