Recognising and mitigating LLM Pollution in online behavioural research

Online behavioural research faces a growing methodological and epistemic threat as participants increasingly rely on large language models: LLM Pollution. Amid accumulating empirical evidence of contamination, we introduce a conceptual framework that distinguishes three variants — Partial LLM Mediation, Full LLM Delegation, and LLM Spillover. Their interaction distorts samples, biases inferences, and fuels an escalating methodological arms race. We outline mitigation strategies spanning researcher practices, platform accountability, and community adaptation.