Advancing Survey Research through AI and Machine Learning: Current Applications and Future Directions |
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| Coordinator 1 | Dr Maud Reveilhac (Departement of Communication and Media Research, Zurich University, Switzerland) |
In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have increasingly been applied in the domain of survey research, offering innovative solutions to long-standing methodological challenges (Shah et al., 2020). AI-powered survey optimization includes adaptive surveys and dynamic questionnaire designs. For instance, AI and ML models are being employed to predict and prevent survey nonresponse and improve respondent engagement by tailoring questions based on real-time data (Buskirk et al., 2020). These tools also help reduce respondent biases by using natural language processing (NLP) to interpret open-ended responses and analyze sentiments more accurately and efficiently (Pandey et al., 2023). Furthermore, data processing and quality control can rely on AI-based methods. For example, ML techniques are being used to detect patterns of low-quality responses, such as speeding and straightlining behaviors (Fernández-Fontelo et al., 2020). Recent advancements also include the use of AI in automating coding and classification tasks for open-ended questions (Zhang et al., 2023), as well as the use of ML for data imputation, enhancing traditional methods by offering more accurate predictions for missing data (Popovich, 2024). These applications highlight the potential of AI and ML to improve survey data quality and reduce labor-intensive tasks. Looking forward, there is significant potential in the integration of AI, which includes: the development of real-time adaptive surveys that adjust to respondent input, the integration of multimodal data collection (integrating voice, image, and text inputs), the assessment of respondent emotions during survey completion, the prediction of future survey behavior (e.g., forecasting nonresponse or disengagement), the integration of AI-powered chatbot for conversational surveys, and the refinement of ethical frameworks surrounding AI's role in survey contexts. This session will explore these topics to define the future of AI in survey research.