University Of Zurich: Research Institution — Prolific Citations Library

Browse 6 peer-reviewed papers from University Of Zurich spanning Generative AI, Disinformation (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 6 peer-reviewed papers from researchers at University Of Zurich in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (6 of 6)

Generative AI and Disinformation| Synthetic Diversity: Examining the Effects of Ethnic Targeting Using AI-Generated Political Ads

Authors: M Wack, DA Parry

Year: 2025

Published in: International Journal of Communication, 2025 - ijoc.org

Institution: University of Zurich

Research Area: Generative AI, Disinformation, Political Communication, Ethnic Targeting

Discipline: Communication, Artificial Intelligence, Political Science

The study finds that AI-generated political ads with coethnic avatars are more effective at mobilizing voter support and reducing skepticism, even when labeled as synthetic, with AI literacy playing a key role in identifying such content.

Methods: Survey experiment targeting voter responses to AI-generated political ads with varied presenter ethnicities, including analysis of AI literacy versus digital literacy.

Key Findings: Effectiveness of coethnic versus out-group ethnic AI-generated avatars in mobilizing voter support and the role of AI literacy in detecting synthetic content.

Citations: 1
A meta-analysis of the persuasive power of large language models

Authors: L Hölbling, S Maier, S Feuerriegel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: University of Lausanne, University of Zurich, University of St. Gallen

Research Area: LLMs in Persuasion, Meta-Analysis, Artificial Intelligence, Human-Computer Interaction (HCI)

Discipline: Artificial Intelligence

Large language models (LLMs) demonstrate similar persuasive performance to humans overall, but their effectiveness varies widely based on contextual factors such as model type, conversation design, and domain.

Methods: Systematic review and meta-analysis using Hedges' g to compute standardized effect sizes, with exploratory moderator analyses and publication bias checks (Egger's test, trim-and-fill analysis).

Key Findings: The persuasive effectiveness of LLMs compared to humans across various contexts and studies.

Sample Size: 17422
Biased By Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers

Authors: Liudmila Zavolokina, Kilian Sprenkamp, Zoya Katashinskaya, Daniel Gordon Jones

Year: 2025

Published in: ArXiv

Institution: University of Zurich

Research Area: AI Ethics, AI Bias, News Literacy, Critical Thinking, Computational Social Science

Discipline: Computational Social Science

The study explores leveraging inherent biases in AI to enhance critical thinking in news consumption, proposing strategies such as bias awareness, personalization, and gradual introduction of diverse perspectives.

Methods: Qualitative user study investigating user responses to personalized AI-driven propaganda detection tools.

Key Findings: The effectiveness of AI bias-based strategies in improving critical thinking and news readers’ engagement with diverse perspectives.
Methods for a fully online automated cognitive training study on Prolific

Authors: F Joessel, S Denkinger, PE Joessel, CS Green

Year: 2025

Published in: Acta Psychologica, 2025 - Elsevier

Institution: Max Planck Institute, University of Potsdam, University of Maryland, University of Zurich, University of Arizona

Research Area: Online cognitive training, Automated psychological studies, Crowdsourcing, behavioral research

Discipline: Psychology

The study introduces a fully online method for conducting cognitive training experiments using Prolific, significantly reducing resource demands while achieving robust results and diverse participant recruitment.

Methods: Participants were recruited via Prolific, assigned to groups using a pseudo-randomized procedure, and completed a 12-hour remote cognitive training study with pre- and post-test assessments monitored via custom dashboards.

Key Findings: Impact of a 12-hour cognitive training intervention on participants' cognitive functions, conducted in a remote and automated manner.
Attributions toward artificial agents in a modified Moral Turing Test

Authors: Eyal Aharoni, Sharlene Fernandes, Daniel J. Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias, Victor Crespo

Year: 2024

Published in: Nature

Institution: Duke University, ETH Zurich, Georgia State University

Research Area: Moral Responsibility, Agency in AI, Human-AI Moral Interaction

Discipline: Artificial Intelligence Ethics
Open problems and fundamental limitations of reinforcement learning from human feedback

Authors: S Casper, X Davies, C Shi, TK Gilbert

Year: 2023

Published in: arXiv preprint arXiv ..., 2023 - arxiv.org

Institution: Columbia University, Cornell Tech, Apollo Research, ETH Zurich, UC Berkeley, University of Sussex, Independent

Research Area: Reinforcement Learning from Human Feedback (RLHF), Alignment, LLM Limitations

Discipline: Artificial Intelligence

DOI: https://doi.org/10.48550/arXiv.2307.15217

Citations: 848