Towards Detecting AI-Assisted Responses in Online Surveys

Abstract:The use of LLMs to complete online surveys impacts the validity of survey-based research, but detecting such usage remains underexplored. We introduce an initial benchmark dataset, namely ASURRE, for AI-assisted survey participation to capture usage strategies ranging from full generation and revision to persona-grounded agentic completion. Controlled by these strategies, LLM-assisted survey responses are generated using multiple LLMs on three real-world surveys in different disciplines, paired with genuine human responses. Our evaluation of existing machine-generated text (MGT) detectors shows that naive AI usage is readily detectable, whereas persona-grounded agents that mimic entire respondents push detector performance toward chance. We further show that agentic completion cannot fully replicate respondent-level behaviour and leaves distinctive behavioural traces. While individual cues can be circumvented by targeted prompting, a simple few-shot, training-free aggregator over these cues improves mean AUROC by +0.14 over the best existing detector across agentic settings. Our project is available at this https URL.

Computation and Language (cs.CL); Computers and Society (cs.CY)

arXiv:2609.17317 [cs.CL]

(or arXiv:2609.17317v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.17317 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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