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
Authors: E Watson, T Viana, S Zhang
Year: 2024
Published in: AI, 2023 - mdpi.com
Research Area: Behavioral Annotation Tools and Multimodal Data
Discipline: Computer Science
The paper systematically reviews augmented behavioral annotation tools, focusing on their evolution, current state, and application to multimodal datasets and models, highlighting best practices and emerging challenges in safe and ethical annotation for large-scale multimodal systems.
Methods: Systematic literature review analyzing crowd and machine learning-augmented behavioral annotation methods, with cross-disciplinary comparisons and structured synthesis of practices.
Key Findings: Evolution of behavioral annotation tools, their integration with machine learning, emerging trends (e.g., prompt engineering), challenges in large multimodal datasets, and ethical and engineering best practices.
DOI: https://doi.org/10.3390/ai4010007
Citations: 17