[
    {
        "id": "osp-23793",
        "type": "article-journal",
        "title": "Towards Detecting AI-Assisted Responses in Online Surveys",
        "author": [
            {
                "family": "Wang",
                "given": "Qizhou"
            },
            {
                "family": "Mamaev",
                "given": "Bogdan"
            },
            {
                "family": "Leckie",
                "given": "Christopher"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/towards-detecting-ai-assisted-responses-in-online-surveys",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "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 https://github.com/mike-qz-wang/ASURRE."
    }
]