One key technique people use in conversation and collaboration is conversational repair. Self-repair is the recognition and attempted correction of one's own mistakes. We investigate how the self-repair of errors by intelligent voice assistants affects user interaction. In a controlled human-participant study with N = 101 participants, participants asked Amazon Alexa to perform four tasks, and we manipulated whether Alexa would ``make a mistake'' understanding the participant (for example, playing heavy metal in response to a request for relaxing music) and whether Alexa would perform a correction (for example, stating, ``You don't seem pleased. Did I get that wrong?'') We measured the impact of self-repair on the participant's perception of the interaction in four conditions: correction (mistakes made and repair performed), undercorrection (mistakes made, no repair performed), overcorrection (no mistakes made, but repair performed), and control (no mistakes made, and no repair performed). Subsequently, we conducted free-response interviews with each participant about their interactions. This study finds that self-repair greatly improves people's assessment of an intelligent voice assistant if a mistake has been made, but can degrade assessment if no correction is needed. However, we find that the positive impact of self-repair in the wake of an error outweighs the negative impact of overcorrection. In addition, participants who recently experienced an error saw increased value in self-repair as a feature, regardless of whether they experienced a repair themselves.
https://doi.org/10.1145/3449101
The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing