Browse 4 peer-reviewed papers from Indiana University spanning Sociological Methods, Generative AI (2024–2025). Research powered by Prolific's high-quality participant data.
This page lists 4 peer-reviewed papers from researchers at Indiana University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: S Zhang, J Xu, AJ Alvero
Year: 2025
Published in: Sociological Methods & Research, 2025 - journals.sagepub.com
Institution: University of Maryland, Indiana University, University of Minnesota Duluth
Research Area: Sociological Methods, Generative AI, Survey Methodology
Discipline: Sociology, Social Science
The study finds that 34% of research participants use generative AI tools like large language models (LLMs) to assist with open-ended survey responses, leading to more homogeneity and positivity in their answers, which could impact data validity by masking social variations.
Methods: The study conducted an original survey on a popular online platform and simulated comparisons between human-written responses from pre-ChatGPT studies and LLM-generated responses.
Key Findings: Use of LLMs by survey participants, differences in text homogeneity, positivity, and masking of social variation in open-ended survey responses.
Citations: 26
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Authors: KHT Vo
Year: 2025
Published in: Design Science, 2025 - cambridge.org
Institution: Indiana University
Research Area: Human-AI Collaboration in Design
Discipline: Human-Computer Interaction (HCI)
This research examines whether a machine, specifically Artificial Intelligence, can be creative by comparing design solutions for a practical competition – a light fixture for a pediatric waiting room – among AI, collaboration efforts and a human designer.
Citations: 1
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Authors: AC Krendl, K Hugenberg, DP Kennedy
Year: 2024
Published in: Behavior research methods, 2024 - Springer
Institution: Indiana University
Research Area: Psychological Research Methods, Data Quality in Online Experiments, Theory of Mind Assessment
Discipline: Research Methodology, Cognitive Psychology
This study found that online samples can reliably complete dynamic, complex theory of mind tasks, though familiarity with task content can influence performance.
Methods: Compared in-lab and online participants' performance on two dynamic theory of mind tasks, using one familiar and one relatively novel TV-based paradigm and counterbalancing task order.
Key Findings: Performance on theory of mind tasks, including inferring beliefs, understanding motivations, detecting deception, identifying faux pas, and understanding emotions.
Citations: 13
Sample Size: 668
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Authors: J Agley
Year: 2024
Published in: Evaluation & the Health Professions, 2025 - journals.sagepub.com
Institution: Indiana University, Prevention Insights
Research Area: Health Research and Evaluation, Data Validity, Computational Social Science
Discipline: Public Health, Computational Social Science
Citations: 2