ESRA 2027 sessions by theme
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Artificial Intelligence in Survey Operative Processes |
| Coordinator 1 | Dr Carina Cornesse (GESIS Leibniz Institute for the Social Sciences) |
| Coordinator 2 | Dr Joachim Piepenburg (GESIS Leibniz Institute for the Social Sciences) |
| Coordinator 3 | Professor Bernd Weiß (University of Duisburg) |
Session Details
In recent years, the scale, costs and complexity of survey data collections have increased considerably. At the same time, so has the demand to collect and publish data fast and within limited budget constraints. These developments stand in conflict with one another and one approach to address these issues is to look for solutions at an operational level, including the use of technologies based on Artificial Intelligence (AI). AI-based solutions are increasingly tested for streamlining workflows, improving participant interactions, and facilitating large-scale project management. In this session, we aim to explore how AI is transforming the full life cycle of survey research operations from planning and deployment to fieldwork and data management. As AI-driven innovations advance rapidly, this session seeks to showcase the most impactful applications and lessons learned on integrating AI into survey operative workflows.
We welcome contributions addressing, but not limited to, the following topics:
• AI in panel survey wave planning, including sample allocation, attrition prediction, scheduling optimization, and resource management.
• AI-enhanced questionnaire programming, including survey scripting, branching logic, and error detection.
• AI in fieldwork monitoring, including techniques for real-time anomaly and fraud detection, respondent engagement tracking, and operational dashboards powered by AI.
• AI in data cleaning and documentation, including error identification, automated metadata and codebook generation, and quality assurance using AI tools.
• AI for participant communication management, including the employment of AI to personalize survey invitations and reminders, optimize contact timing and channels, manage follow-ups, and automate communication workflows for greater engagement and retention.
Particularly encouraged are papers presenting scalable solutions, transparent methodologies, open-source tools, replicable workflows, or critical reflections on challenges and ethical considerations in deploying AI for survey operations.
