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University Of North Carolina At Charlotte Papers

0 research papers from University Of North Carolina At Charlotte that used Prolific for participant recruitment.

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University Of North Carolina At Charlotte

University Of North Carolina At Charlotte: Research Institution — Prolific Citations Library

Browse 3 peer-reviewed papers from University Of North Carolina At Charlotte spanning Human-Computer Interaction, LLM behavior (2023–2025). Research powered by Prolific's high-quality participant data.

This page lists 3 peer-reviewed papers from researchers at University Of North Carolina At Charlotte in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (3 of 3)

When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine

Authors: L Ma, J Qin, X Xu, Y Tan

Year: 2025

Published in: arXiv preprint arXiv:2509.14436, 2025•arxiv.org

Institution: University of North Carolina Charlotte, University of Science and Technology of China, University of Washington

Research Area: LLM behavior, Algorithmic content preference, Human-AI Interaction

Discipline: Computer Science, Information Retrieval, Artificial Intelligence

This paper studies how generative search engines that use large language models (LLMs)—like Google’s AI overviews—select and cite web content, showing that these engines prefer content that is more predictable and semantically coherent for the model, and that LLM-based content polishing can increase the diversity and usefulness of AI summaries for users.

DOI: https://doi.org/10.48550/arXiv.2509.14436
I know this looks bad, but I can explain: Understanding when AI should explain actions in human-AI teams

Authors: R Zhang, C Flathmann, G Musick, B Schelble

Year: 2024

Published in: ACM Transactions on ..., 2024 - dl.acm.org

Institution: North Carolina State University, University of North Carolina at Charlotte, University of Georgia, University of Michigan

Research Area: Explainable AI (XAI), Human-AI Teaming, Human-Computer Interaction

Discipline: Robotics, Artificial Intelligence

Explored how AI explanations impact human trust and team effectiveness in human-AI teams, finding that explanations increase trust when AI disobeys orders but reduce trust when AI lies, with individual characteristics influencing these perceptions.

Methods: Conducted an online experiment analyzing participant responses to scenarios where AI explained its actions within a teamwork context, comparing trust in AI versus human teammates.

Key Findings: Impact of AI explanations on human trust, team effectiveness, and how these vary with teammate identity (human or AI) and participant characteristics (e.g., gender, ethical framework).

DOI: https://doi.org/10.1145/3635474

Citations: 28

Sample Size: 156
The value of AI guidance in human examination of synthetically-generated faces

Authors: A Boyd, P Tinsley, K Bowyer, A Czajka

Year: 2023

Published in: Proceedings of the AAAI ..., 2023 - ojs.aaai.org

Institution: University of Notre Dame, Clemson University, University of North Carolina at Charlotte

Research Area: Computer Vision, Human-Computer Interaction, Synthetic Media Detection

Discipline: Artificial Intelligence, Computer Vision, Human-Computer Interaction

Citations: 20