Explore 4 peer-reviewed studies by Z Chen in Artificial Intelligence and Social Science and Persuasion Studies (2024–2025). Discover research powered by Prolific's participant panel.
This page lists 4 peer-reviewed papers authored or co-authored by Z Chen in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: Z Chen, J Kalla, Q Le, S Nakamura-Sakai
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: The affiliated institutions could not be determined from the provided context or an external search of the URL.
Research Area: Artificial Intelligence and Social Science, Persuasion Studies, Political Persuasion, LLM Chatbots, Democratic Societies
Discipline: Artificial Intelligence, Social Science
The study evaluates the cost-effectiveness and persuasive risks of Large Language Model (LLM) chatbots in political contexts, finding that while LLMs are as persuasive as campaign ads under exposure, their large-scale influence is currently limited by scalability and cost barriers.
Methods: Two survey experiments combined with real-world simulation exercises to measure the persuasiveness of LLM chatbots compared to traditional campaign tactics, focusing on both exposure and acceptance phases of persuasion.
Key Findings: Short- and long-term persuasive effects of LLMs, cost-effectiveness of LLM-based persuasion ($48-$74 per persuaded voter), and scalability compared to traditional campaign approaches.
Citations: 7
Sample Size: 10417
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Authors: Z Cheng, J You
Year: 2025
Published in: arXiv preprint arXiv:2509.22989, 2025 - arxiv.org
Institution: University of Southern California, University of California Berkeley
Research Area: Artificial Intelligence, Computers and Society, Computer Science and Game Theory, Strategic Persuasion, Reinforcement Learning, Language Models, LLM, RLHF
Discipline: Artificial Intelligence
This paper introduces a scalable framework, utilizing Bayesian Persuasion, to evaluate and train LLMs for strategic persuasion, demonstrating significant persuasion gains and effective strategies through reinforcement learning.
Methods: Repurposed human-human persuasion datasets for evaluation and training; applied Bayesian Persuasion framework; used reinforcement learning to optimize LLMs for strategic persuasion.
Key Findings: The persuasive capabilities and strategies of large language models (LLMs) in various settings.
Citations: 1
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Authors: Z Chen, J Chan
Year: 2024
Published in: Management Science, 2024 - pubsonline.informs.org
Institution: University of Texas Dallas
Research Area: Human-AI Interaction, Creative Work, Behavioral Science
Discipline: Social Science
Using large language models (LLMs) as sounding boards improves ad content quality for nonexpert users, while using LLMs as ghostwriters can negatively impact expert users due to anchoring effects.
Methods: An experiment comparing ad copy creation with and without LLM assistance, focusing on two collaboration modalities: ghostwriting and sounding board approaches. Ad performance was measured via social media click rates, supported by textual analysis.
Key Findings: Effectiveness of LLM collaboration modalities (ghostwriting vs. sounding board) on ad quality and business outcomes for expert and nonexpert users.
DOI: https://doi.org/10.1287/mnsc.2023.03014
Citations: 180
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Authors: KD Wang, Z Chen, C Wieman
Year: 2024
Published in: ... of the 14th Learning Analytics and ..., 2024 - dl.acm.org
Institution: Delft University of Technology, University of Queensland
Research Area: Crowdsourcing for Educational Research
Discipline: Educational Research, Computer Science
Citations: 8