Robot Duck Debugging: Can Attentive Listening Improve Problem Solving?

TL;DR

This study examined whether a social robot's attentive listening could enhance problem-solving during think-aloud sessions, finding no significant improvement over a non-interactive object, and exploring implications for robot-assisted cognitive tasks.

Contribution

It is the first to compare rule-based and deep-learning robot listening behaviors in a rubber duck debugging context, assessing their impact on problem-solving and user experience.

Findings

Robot presence did not improve task performance compared to a static object.

Neither listening behavior significantly affected user engagement or perception.

The study highlights challenges in designing social robots for cognitive support.

Abstract

While thinking aloud has been reported to positively affect problem-solving, the effects of the presence of an embodied entity (e.g., a social robot) to whom words can be directed remain mostly unexplored. In this work, we investigated the role of a robot in a "rubber duck debugging" setting, by analyzing how a robot's listening behaviors could support a thinking-aloud problem-solving session. Participants completed two different tasks while speaking their thoughts aloud to either a robot or an inanimate object (a giant rubber duck). We implemented and tested two types of listener behavior in the robot: a rule-based heuristic and a deep-learning-based model. In a between-subject user study with 101 participants, we evaluated how the presence of a robot affected users' engagement in thinking aloud, behavior during the task, and self-reported user experience. In addition, we explored the impact of the two robot listening behaviors on those measures. In contrast to prior work, our results indicate that neither the rule-based heuristic nor the deep learning robot conditions improved performance or perception of the task, compared to an inanimate object. We discuss potential explanations and shed light on the feasibility of designing social robots as assistive tools in thinking-aloud problem-solving tasks.