Human Ai Interaction: Research Area — Prolific Citations Library

Discover 46 peer-reviewed studies in Human Ai Interaction (2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 46 peer-reviewed papers in the research area of Human Ai Interaction in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 46)

Fostering appropriate reliance on large language models: The role of explanations, sources, and inconsistencies

Authors: SSY Kim, JW Vaughan, QV Liao, T Lombrozo

Year: 2025

Published in: Proceedings of the ..., 2025 - dl.acm.org

Institution: Wake Forest University, University of Illinois at Urbana-Champaign, Princeton University, University of California Berkeley

Research Area: Appropriate Reliance on LLMs, Explainable AI, Human-AI Interaction, Cognitive Psychology

Discipline: Cognitive Psychology, Artificial Intelligence, Human-Computer Interaction (HCI)

The study examines factors that influence users' reliance on LLM responses, finding explanations increase reliance, while sources and inconsistent explanations reduce reliance on incorrect responses.

Methods: Think-aloud study followed by a pre-registered, controlled experiment to assess the impact of explanations, sources, and inconsistencies in LLM responses on user reliance.

Key Findings: Users' reliance on LLM responses, accuracy, and the influence of explanations, inconsistencies, and sources on these measures.

DOI: https://doi.org/10.1145/3706598.3714020

Citations: 38

Sample Size: 308
Can large language models assess personality from asynchronous video interviews? A comprehensive evaluation of validity, reliability, fairness, and rating patterns

Authors: T Zhang, A Koutsoumpis, JK Oostrom

Year: 2025

Published in: IEEE Transactions ..., 2024 - ieeexplore.ieee.org

Institution: Southeast University, Vrije Universiteit, Tilburg University

Research Area: LLM Personality Assessment, Human-AI Interaction, LLM

Discipline: Human-AI Interaction, Social Science, Humanities

LLMs like GPT-3.5 and GPT-4 can rival or outperform task-specific AI models in assessing personality traits from asynchronous video interviews, but show uneven performance, low reliability, and potential biases, warranting cautious use in high-stakes scenarios.

Methods: The study evaluated GPT-3.5 and GPT-4 performance in assessing personality traits and interview performance using simulated AVI responses, comparing them with ratings from task-specific AI and human annotators.

Key Findings: Validity, reliability, fairness, and rating patterns of LLMs (GPT-3.5 and GPT-4) in personality assessment from asynchronous video interviews.

Citations: 31

Sample Size: 685
How do people react to political bias in generative Artificial Intelligence?

Authors: U Messer

Year: 2025

Published in: Computers in Human Behavior: Artificial Humans, 2025 - Elsevier

Institution: Universität der Bundeswehr München

Research Area: Political Bias in Generative AI, Human-AI Interaction, Affective Computing, AI Bias

Discipline: Computer Science, Human-AI Interaction

People's acceptance and reliance on Generative AI (GAI) increase when they perceive alignment between their political orientation and the bias of GAI-generated content, leading to expanded trust in sensitive applications.

Methods: Three experiments analyzing behavioral reactions to politically biased content generated by GAI, including the impact of perceived alignment on acceptance and trust.

Key Findings: Participants' acceptance, reliance, and trust in GAI based on perceived alignment between political bias of GAI-generated content and their own political beliefs.

DOI: https://doi.org/10.1016/j.chbah.2024.100108

Citations: 24

Sample Size: 513
Cognitive Forcing for Better Decision-Making: Reducing Overreliance on AI Systems Through Partial Explanations

Authors: S de Jong, V Paananen, B Tag

Year: 2025

Published in: Proceedings of the ACM on ..., 2025 - dl.acm.org

Institution: Niels van Berkel: Aalborg University, Sander de Jong, Ville Paananen, Benjamin Tag: Monash University

Research Area: Cognitive Forcing, Human-AI Interaction, AI Explainability (XAI), Decision-Making in AI Systems.

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence

Partial explanations encourage critical thinking and reduce user overreliance on incorrect AI suggestions, with performance varying based on individual need for cognition and task difficulty.

Methods: Two experiments were conducted: (1) participants identified shortest paths in weighted graphs, and (2) participants corrected spelling and grammar errors in text, with AI suggestions accompanied by no, partial, or full explanations.

Key Findings: Effectiveness of partial explanations in reducing overreliance on incorrect AI suggestions, and interaction of explanation type with task difficulty and user need for cognition.

DOI: https://doi.org/10.1145/3710946

Citations: 14

Sample Size: 474
Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey ...

Authors: C Chen, Z Cui

Year: 2025

Published in: Journal of Medical Internet Research, 2025 - jmir.org

Institution: Medical College of Wisconsin

Research Area: Trust in AI, AI-assisted diagnosis, Health communication, Healthcare human-AI interaction

Discipline: Digital Health, Human-Computer Interaction (HCI), Behavioral Science

Patients trust and are more likely to seek help from doctors explicitly avoiding AI-assisted diagnosis rather than those using extensive or moderate AI, highlighting a strong aversion to AI in healthcare settings.

Methods: A randomized, web-based 4-group survey experiment was conducted with controls for sociodemographic factors and analysis using regression, mediation, and moderation techniques.

Key Findings: Trust in and intention to seek medical help from health care professionals using AI-assisted diagnosis versus those avoiding AI, and the influence of demographic, social, and experiential factors.

DOI: https://doi.org/10.2196/66083

Citations: 4

Sample Size: 1762
Associative Memory and Trustworthiness of Artificial Faces in Young and Older Adults

Authors: KO Alberts, AD Castel

Year: 2025

Published in: Experimental Aging Research, 2025 - Taylor & Francis

Institution: University of California Los Angeles

Research Area: Cognitive Aging, Associative Memory, Trustworthiness of Artificial Faces, Human-AI Interaction, Psychology, Trust in AI

Discipline: Psychology, Psychobiology, Aging Research

Older adults perceive artificial faces as equally trustworthy as real faces, unlike young adults who find artificial faces less trustworthy, and older adults show no difference in memory accuracy between face types.

Methods: Participants viewed real and artificial faces associated with scam or neutral conditions, then rated trustworthiness and were tested on associative memory.

Key Findings: Associative memory and perceived trustworthiness of real and artificial faces across young and older adults.

Citations: 1
Deliberate Lab: A Platform for Real-Time Human-AI Social Experiments

Authors: C Qian, V Tsai, M Behr, N Hussein, L Laugier, N Thain, L Dixon

Year: 2025

Published in: ArXiv

Institution: Google, Google DeepMind, EPFL

Research Area: Human-AI Interaction, Social Experiments, Platform Design

Discipline: Computational Social Science

Deliberate Lab is an open-source platform designed to enable real-time, multi-user human and AI (LLM) experiments. Developed by DeepMind researchers, it supports synchronous interaction, custom experimental stages, and integrates with platforms like Prolific for streamlined participant recruitment and payment. The system has been successfully used in over 600 experiments with more than 9,000 pa...

Citations: 1
Do People Think Fast or Slow When Training AI?

Authors: L S. Treiman, CJ Ho, W Kool

Year: 2025

Published in: Proceedings of the 2025 ACM Conference ..., 2025 - dl.acm.org

Institution: Washington University in St. Louis, National Cheng Kung University

Research Area: Human-AI Interaction, Cognitive Science, Behavioral Research in AI Training

Discipline: Human-Computer Interaction (HCI), Behavioral Science

Participants tend to rely on intuition (fast thinking) rather than deliberation (slow thinking) when training AI agents in the ultimatum game, impacting human-AI collaboration system design.

Methods: Participants trained an AI agent in the ultimatum game to analyze whether their training decisions aligned more with intuitive or deliberative cognitive processes.

Key Findings: The cognitive processes (fast vs. slow thinking) underlying human decision-making during AI training.

DOI: https://dl.acm.org/doi/abs/10.1145/3715275.3732177

Citations: 1
Factors Shaping Perceptions of AI Tools Among a Nationally Representative Sample of US Adults

Authors: B Katz, N Abdelgawad, D Friedberg, P Roberts, S Misra

Year: 2025

Published in: Innovation in Aging, 2025•pmc.ncbi.nlm.nih.gov

Institution: Virginia Tech

Research Area: Human–AI Interaction (HCI), Technology Perception

Discipline: Behavioral Science

Age significantly influences perceptions of generative AI tools, with older individuals perceiving more benefits and fewer risks compared to younger individuals; thinking dispositions also play a role.

Methods: A nationally representative survey of US adults conducted via the Prolific platform using various AI-relevant scales, including attitudes, risks, benefits, frequency of use, expertise, and literacy assessments.

Key Findings: Demographic factors, industry types, thinking dispositions, and attitudes toward generative AI tools, including risk and utility perceptions.

Citations: 1

Sample Size: 500
Human-AI Collaboration in High-Stakes Decision-Making: Work in Progress

Authors: J Zhou, R Aloufi, N van Zalk

Year: 2025

Published in: 38th International BCS Human ..., 2025 - scienceopen.com

Institution: The affiliated institutions for the authors are not available in the provided context or search results.

Research Area: High-Stakes Decision-Making, Explainable AI, User Trust, Human-Centered AI, Interaction Design

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence

This study explores how human collaboration and communication dynamics vary when interacting with an AI chatbot versus a human partner in a high-stakes decision-making task.

Methods: One-way between-subjects design using the NASA Moon Survival Task to compare behaviors, linguistic coordination, and perceptions in interactions with AI or human partners.

Key Findings: Collaboration processes, communicative dynamics, outcomes, retrospective interaction experience, partner perception, and linguistic coordination, with user profiling for AI benefit variations.

DOI: doi.org/10.14236/ewic/BCSHCI2025.52

Citations: 1
Trust in AI is dynamically updated based on users' expectations

Authors: P Cooper, A Lim, J Irons, M McGrath, H Jarvis

Year: 2025

Published in: Proceedings of the ..., 2025 - dl.acm.org

Institution: Microsoft Research, Massachusetts Institute of Technology, University of Washington

Research Area: Human-AI Interaction, Trust in AI

Discipline: Human-Computer Interaction (HCI)

Trust in AI dynamically influences users' reliance on AI advice during a deepfake detection task, with no significant impact observed from the timing of AI advice delivery.

Methods: Researchers conducted an online study with participants performing a deepfake detection task, comparing performance across conditions where AI advice was provided either concurrently with decisions or after an initial evaluation. Computational modeling was used to analyze trust dynamics.

Key Findings: Impact of AI advice and its timing on task performance, and the dynamic role of user trust in AI based on expectations of its ability.

DOI: https://dl.acm.org/doi/10.1145/3706599.3719870

Citations: 1
Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots

Authors: Z Ashktorab, A Buccella, J D'Cruz, Z Fowler, A Gill, KY Leung, PD Magnus, J Richards

Year: 2025

Published in: arXiv preprint arXiv:2507.02745, 2025•arxiv.org

Institution: IBM Research, University at Albany

Research Area: Human–AI interaction, AI systems evaluation, UX, User Experience

Discipline: Computer Science, Human–Computer Interaction (HCI)

In a preregistered study with 162 participants, people generally prefer explanatory apologies from LLM chatbots over rote or purely empathic ones—though in biased error scenarios empathic apologies are sometimes favored—highlighting the complexity of designing chatbot apologies that effectively repair trust.

DOI: https://doi.org/10.48550/arXiv.2507.02745

Citations: 1
Whose view of safety? a deep dive dataset for pluralistic alignment of text-to-image models

Authors: C Rastogi, TH Teh, P Mishra, R Patel, D Wang, M Díaz, A Parrish, AM Davani, Z Ashwood

Year: 2025

Published in: arXiv preprint arXiv:2507.13383, 2025•arxiv.org

Institution: Google DeepMind, Google Research, Google

Research Area: AI alignment, safety evaluation, AI Safety, Multimodal evaluation, Human–AI interaction, LLM

Discipline: Computer Science, Machine Learning, Artificial Intelligence

This research introduces the DIVE dataset to enable pluralistic alignment in text-to-image models by accounting for diverse safety perspectives, revealing demographic variations in harm perception and advancing T2I model alignment strategies.

Methods: The study involved collecting feedback across 1000 prompts from demographically intersectional human raters to capture diverse safety perspectives, with an emphasis on empirical and contextual differences in harm perception.

Key Findings: Safety perceptions of text-to-image (T2I) model outputs from diverse demographic viewpoints and the influence of these perspectives on alignment strategies.

Citations: 1

Sample Size: 1000
A Descriptive and Normative Theory of Human Beliefs in RLHF

Authors: S Dandekar, S Deshmukh, F Chiu, WB Knox

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: University of California, Davis, Northwestern University

Research Area: Reinforcement Learning from Human Feedback (RLHF), Human-AI Interaction, AI Theory

Discipline: Artificial Intelligence, Social Science

The paper investigates how human beliefs about agent capabilities influence preferences in RLHF, proposing a model to minimize the mismatch between beliefs and idealized agent capabilities, ultimately improving policy performance.

Methods: Human studies and synthetic experiments to model and test the impact of belief mismatches on human preferences and RLHF effectiveness.

Key Findings: Effects of human beliefs about agent capabilities on their provided preferences and the performance of RLHF policies.

DOI: https://doi.org/10.48550/arXiv.2506.01692
Artificial minds and real beliefs: Perceiving mental states in AI

Authors: O Jacobs

Year: 2025

Published in: 2025 - open.library.ubc.ca

Institution: University of British Columbia

Research Area: Mind Perception in Human-AI Interaction, Anthropomorphism, Psychology

Discipline: Psychology, Human-Computer Interaction (HCI) in AI

This is a University of British Columbia doctoral thesis that investigates how people perceive and attribute mental states (beliefs, intentions, minds) to artificial intelligence systems — exploring the psychological and conceptual underpinnings of mind perception in human–AI interaction.
Beyond Face Value: Visual and Auditory Signals in Human and Machine Trust Judgments

Authors: N Tyulina, Y Yu, TA Emmanouil, SI Levitan

Year: 2025

Published in: Proceedings of the 7th ACM ..., 2025 - dl.acm.org

Institution: University of Cambridge, University of Bath, University of Edinburgh, New York University

Research Area: Human-AI Interaction, Trust and Perception, Nonverbal Communication

Discipline: Applied Linguistics

Trust judgments are primarily influenced by auditory cues in both humans and multimodal models, though subtle differences in modality weighting exist between them.

Methods: Behavioral experiment with trust ratings of bimodal stimuli across four trust congruence conditions, combined with a multimodal model trained using HuBERT and ResNet-50 with late fusion, analyzed using Permutation Feature Importance (PFI).

Key Findings: The construction of trust from visual and auditory signals in both humans and multimodal models, focusing on modality dominance and feature weighting.

Sample Size: 150
Blissful (A)Ignorance: People form overly positive impressions of others based on their written messages, despite wide-scale adoption of Generative AI

Authors: Jiaqi Zhua, Andras Molnar

Year: 2025

Published in: ArXiv

Institution: University of Michigan

Research Area: Social Psychology, Human-AI Interaction, Generative AI Impact on Social Perception

Discipline: Social Science, Social Psychology, Human-Computer Interaction (HCI)

Impressions of written messages are overly positive when recipients are unaware of potential Generative AI (GenAI) use, but negative when GenAI use is explicitly disclosed.

Methods: A pre-registered large-scale online experiment leveraged Prolific participants to assess social impressions in diverse communication contexts, with varying levels of sender disclosure regarding GenAI use.

Key Findings: The influence of known or uncertain GenAI use on recipients' social impressions of message senders across different personal and professional contexts.

Sample Size: 647
Comparing discriminatory behavior against AI and humans

Authors: M Zhuang, E Deschrijver, R Ramsey, O Turel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: Monash University, The University of Melbourne, KU Leuven, California State University Fullerton

Research Area: Human-AI Interaction, Social Bias, Decision-Making

Discipline: Social Science, Human-AI Interaction

The study found that humans exhibit similar discriminatory behavior toward both AI and human agents, with resource allocation being influenced more by decision alignment than the recipient's identity.

Methods: A preregistered experiment was conducted where participants distributed resources between themselves and either human or AI agents based on dot estimation decisions.

Key Findings: Discriminatory behavior and resource allocation preferences toward AI and human agents as influenced by decision congruency.

DOI: https://doi.org/10.1038/s41598-025-94631-9

Sample Size: 500
Improving Experimental Methods to Capture Real-World Human-AI Perceptions and Interactions

Authors: N Haduong

Year: 2025

Published in: 2025 - search.proquest.com

Institution: University of Washington

Research Area: Human-AI Interaction and Perception

Discipline: Human-Computer Interaction (HCI)

The research focuses on developing methodologies to bridge the gap between controlled laboratory studies and real-world human-AI perceptions and interactions, promoting task immersion and intrinsic motivation to model realistic behaviors.

Methods: Used task immersion techniques, domain-specific recruitment, error taxonomy development, and CPS-TaskForge environment generator for systematic study of collaborative problem solving and AI-assisted decision-making.

Key Findings: Human perceptions of AI in collaborative problem solving, understanding risks in AI-assisted decision making, and user behavior under performance pressure with AI advice.
Improving annotation quality: empirical insights into bias, human-AI collaboration, and workflow design

Authors: J Beck

Year: 2025

Published in: 2025 - edoc.ub.uni-muenchen.de

Institution: Ludwig-Maximilians-Universität München, University of Bayreuth

Research Area: Annotation Quality, Human-AI Collaboration, Behavioral Science, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI)

The study empirically evaluates annotation bias, proposes strategies to reduce its impact, and explores the use of large language models in automated and hybrid annotation workflows.

Methods: Empirical assessments and experimental evaluations involving annotation workflows and large language models.

Key Findings: Annotation bias, annotation quality, and the effectiveness of hybrid workflows integrating human input and AI models.