Evaluating applications of generative artificial intelligence in questionnaire design, evaluation and testing |
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| Coordinator 1 | Dr Caroline Roberts (University of Lausanne) |
| Coordinator 2 | Professor Patrick Sturgis (London School of Economics and Political Science) |
| Coordinator 3 | Dr Tom Robinson (London School of Economics and Political Science) |
| Coordinator 4 | Ms Alice McGee (Verian Group (UK)) |
It is widely agreed that Generative Artificial Intelligence (GenAI) will transform conventional practice across the spectrum of service industries in the near future. Since the launch of OpenAI’s ChatGPT in November 2022, GenAI applications have already radically impacted work practices across multiple sectors, and their potential to revolutionise survey research has quickly been acknowledged, and started to be investigated empirically. The commercial sector, especially, has been embracing the opportunities GenAI can offer market research practice, and the past two years have seen a mushrooming of new platforms and tools based on custom-trained generative models, and particularly, Large Language Models (LLMs). These offer a broad range of solutions, from automated sampling and recruitment; synthetic data generation and augmentation; opinion and behaviour prediction and forecasting; data cleaning and validation; qualitative and quantitative data analysis; to questionnaire and dynamic survey design. However, researchers responsible for the implementation of high quality, academic and government surveys have been more sceptical and cautious about the utility and effectiveness of some of these GenAI applications, as well as about their ethical implications. Nevertheless, there is growing recognition of the urgency for research to investigate and evaluate the opportunities and risks this transformative general purpose technology can offer, and to appropriately anticipate its likely disruptive impact on current practice. In this session, we invite presentations of research investigating diverse applications of Gen-AI in the different steps involved in survey questionnaire design, and in their evaluation and testing (QDET). We encourage submissions from researchers from a broad range of sectors who are currently engaged in evaluating and validating alternative Gen-AI models and developing custom tools with the potential to transform current QDET practice in high quality, general population, probability-based sample surveys in high quality surveys.