Introduction
This dataset consists of analyzed results of short interview responses about imagined encounters with robots in high-traffic pedestrian spaces within a university campus. The interviews focused on non-robot deployment scenarios, exploring human expectations and reactions to robots. Participants were asked to describe how they might interact with robots in various public environments, resulting in discursive accounts that explore a wide range of potential human-robot interactions in public spaces. The research design prioritized discursive accounts and employed analysis based on Grounded Theory to uncover insights into human attitudes toward robots. To ensure the utmost privacy for the interviewees, the dataset has been carefully curated to include only analysis-ready content, with all personally identifiable information removed. The interview protocol is also included.
By combining perspectives from Communication Studies, Human-Robot Interaction, and Information Studies disciplines the interview protocol and data analysis results contribute actionable insights to the field of Human-Robot Interaction (HRI), particularly in terms of understanding user personas, agency dynamics, and design considerations for future robotic systems in public spaces.
Dataset Contents
- Interview protocol and questionnaire.
- Thematic Analysis Results: Results from thematic analysis of the interview data.
- Poster describing research motivation and methodology
Participants
The study included responses from 41 participants who were interviewed in a public pedestrian area at the University of Texas at Austin. These participants represented a diverse cross-section of the university community. All participants provided informed consent prior to the interviews, and to protect their privacy, the data was anonymized, with no identifying personal details included in the analysis.
Interview Protocol and Questionnaire
The interviews lasted between 8 and 16 minutes and were conducted in a densely populated pedestrian area on campus. This setting was chosen to simulate real-world environments where people might encounter robots in the future. The interview protocol involved asking participants to imagine a robot encounter and explore their thoughts on robot functionality, behavior, and interaction. Researchers encouraged participants to elaborate on their ideas, capturing rich insights about human-robot interaction in public spaces.
The interviews were semi-structured, focusing on participants' imagined encounters with robots in their current environments. Participants were asked to reflect on their expectations, concerns, and desires regarding robots. Follow-up questions explored the specific roles they envisioned for robots, their fears, and the features they hoped to see in robotic systems. This approach was designed to capture spontaneous reactions while encouraging deeper reflection on human-robot interaction.
Data Analysis Method
Researchers adopted a constructivist Grounded Theory approach for data analysis. More specifically, the data was analyzed using socio-technical grounded theory, allowing the researchers to build themes from participants’ discursive accounts. Based on these accounts, researchers identified a framework of human-robot agency. Primarily, the framework focuses on a set of agency dynamics that reflect the imagined scenarios participants described in interviews. Through the analysis, researchers also developed possible user personas which reflect described actors in participant scenarios, and a checklist for designers to use when considering ethical dilemmas prior to deployment.
Access Restrictions
The interview transcripts are not published for purposes of protecting the identity of the participants.
Acknowledgment
This study was funded through the NSF Award # 2219236GCR: Community-Embedded Robotics: Understanding Sociotechnical Interactions with Long-term Autonomous Deployments.
