Discover 52 peer-reviewed studies in Human Computer Interaction Hci (2024–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 52 peer-reviewed papers in the research area of Human Computer Interaction Hci in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: F Salvi, M Horta Ribeiro, R Gallotti, R West
Year: 2025
Published in: Nature Human Behaviour, 2025 - nature.com
Institution: EPFL, Fondazione Bruno Kessle, Princeton University
Research Area: Conversational Persuasion of LLM, Human-Computer Interaction (HCI), Behavioral Science, LLM
Discipline: Behavioral Science
GPT-4 can use personalized arguments to be more persuasive in debates, outperforming humans in 64.4% of AI-human comparisons when personalization is applied.
Methods: Preregistered controlled study involving multiround debates with random assignment to conditions focusing on AI-human comparisons, personalization, and opinion strength.
Key Findings: Effectiveness of persuasion by GPT-4, especially when using personalized arguments, compared to humans in debates.
Citations: 65
Sample Size: 900
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Authors: M Riveiro, S Thill
Year: 2025
Published in: Proceedings of the 30th ACM Conference on User ..., 2022 - dl.acm.org
Institution: Linköping University, University of Skövde
Research Area: Explainable AI, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI)
Users prefer factual explanations when AI outputs match expectations and mechanistic explanations when outputs deviate, with preferences influenced by response format (multiple-choice vs free text).
Methods: Participants were presented with scenarios involving an automated text classifier and asked to express their preference for explanations either through multiple-choice or free text responses.
Key Findings: User-desired content of AI explanations based on whether system behaviour aligns or deviates from expectations.
DOI: 10.1145/3503252.3531306
Citations: 30
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Authors: Y Ding, J You, TK Machulla, J Jacobs, P Sen
Year: 2025
Published in: Proceedings of the ..., 2022 - dl.acm.org
Institution: University of California Irvine, University of Florida, State University of New York at Buffalo, University of Waterloo, Virginia Tech
Research Area: Computational Social Science, Human-Computer Interaction (HCI), Sentiment Analysis
Discipline: Computational Social Science
Demographic differences among annotators significantly affect sentiment dataset labels, causing up to a 4.5% accuracy difference in sentiment prediction models.
Methods: Crowdsourced annotations from >1000 workers combined with demographic data; analysis of multimodal sentiment datasets and evaluation using machine learning models.
Key Findings: Impact of annotator demographics on sentiment labeling and its effect on model predictions.
DOI: https://doi.org/10.1145/3555632
Citations: 28
Sample Size: 1000
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Authors: JY Bo, S Wan, A Anderson
Year: 2025
Published in: Proceedings of the 2025 CHI Conference ..., 2025 - dl.acm.org
Institution: University of Toronto
Research Area: Appropriate reliance on LLM, Human-Computer Interaction (HCI), AI-assisted decision making.
Discipline: Human-Computer Interaction (HCI)
This paper explores the latest advancements and key trends in the field of Human-Computer Interaction (HCI), focusing on novel interfaces and user experience paradigms.
Citations: 25
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Authors: K Zhou, JD Hwang, X Ren, N Dziri
Year: 2025
Published in: Proceedings of the ..., 2025 - aclanthology.org
Institution: Stanford University, University of Southern California, Carnegie Mellon University, Allen Institute for AI
Research Area: Human-LM Reliance, Interaction-Centered Framework, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
The study introduces Rel-A.I., an interaction-centered evaluation approach to measure human reliance on LLM responses, revealing that politeness and interaction context significantly influence user reliance.
Methods: Nine user studies were conducted, analyzing user reliance influenced by LLM communication features such as politeness and context through participant interaction experiments.
Key Findings: The degree of human reliance on LLM responses based on communication style (e.g., politeness) and interaction context (e.g., knowledge domain, prior interactions).
Citations: 18
Sample Size: 450
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Authors: A Okoso, K Otaki, S Koide, Y Baba
Year: 2025
Published in: ACM Transactions on Recommender Systems, 2025•dl.acm.org
Institution: Toyota Central R and D Labs, Toyota
Research Area: Human-Computer Interaction (HCI)
Discipline: Machine Learning, Artificial Intelligence
The study demonstrates that tailoring the tone of textual explanations in recommender systems to domains and user attributes, such as age and personality traits, can enhance users' perceptions and engagement.
Methods: Two online user studies: (1) 470 participants evaluated synthetic explanations with six tones across three domains (movies, hotels, and home products), (2) 103 participants engaged with a real-world dataset from the hotel domain using a personalized recommender system.
Key Findings: The perceived effects of different textual explanation tones on users, examined across domains (movies, hotels, home products) and user attributes (e.g., age, personality traits).
DOI: https://dl.acm.org/doi/10.1145/3718101
Citations: 13
Sample Size: 573
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Authors: J Li, M Kuutila, E Huusko, N Kariyakarawana
Year: 2025
Published in: Proceedings of the 15th ..., 2023 - dl.acm.org
Institution: University of Oulu
Research Area: Social Media Credibility, Crowdsourcing, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI)
Credibility of short-form health-related social media posts is influenced by factors such as author profession and post engagement metrics, with experts being encouraged to actively participate in information correction online.
Methods: Crowdsourced online credibility assessment using health-themed social media posts with varied content features deployed across three platforms; quantitative and qualitative data collection.
Key Findings: Credibility factors like author profession, engagement metrics (likes/shares), and personal strategies influencing perceived trustworthiness of social media posts.
DOI: 10.1145/3605390.3605406
Citations: 11
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Authors: AG Møller, DM Romero, D Jurgens
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: University of Copenhagen, University of Michigan, Pioneer Centre for AI
Research Area: Generative AI, Social Media, Human-Computer Interaction (HCI)
Discipline: Computational Social Science
Generative AI tools on social media increase user engagement and content volume but reduce perceived quality and authenticity in discussions, highlighting challenges for ethical integration.
Methods: Controlled experiment with participants assigned to small discussion groups under distinct AI-assisted treatment conditions including chat assistance, conversation starters, feedback on comment drafts, and reply suggestions.
Key Findings: Impact of generative AI tools on user behavior, engagement, content volume, perceived quality, and authenticity in social media interactions.
DOI: https://doi.org/10.48550/arXiv.2506.14295
Citations: 9
Sample Size: 680
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Authors: J Beck, S Eckman, C Kern, F Kreuter
Year: 2025
Published in: arXiv preprint arXiv:2509.08514, 2025 - arxiv.org
Institution: National Institutes of Health, National Center for Biotechnology Information
Research Area: Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI)
Human attitudes toward AI strongly influence performance in collaborative tasks, with skeptics showing better error detection and accuracy, while automation favorability increases overreliance on AI suggestions.
Methods: Randomized experiment with a controlled annotation task manipulating AI suggestion quality, task burden, and performance-based financial incentives; collected demographic, attitudinal, and behavioral data.
Key Findings: Impact of AI suggestion quality, task burden, and financial incentives on participant performance metrics (accuracy, correction activity, overcorrection, undercorrection); influence of demographic and psychological characteristics on performance.
Citations: 4
Sample Size: 2784
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Authors: G Riva, BK Wiederhold, P Cipresso
Year: 2025
Published in: ... , Behavior, and Social ..., 2025 - liebertpub.com
Institution: Università Cattolica del Sacro Cuore, University of Genova, Università degli Studi di Milano, Università di Catania
Research Area: AI Ethics, Social and Psychological Dimensions of Artificial Intelligence, Human-Computer Interaction (HCI)
Discipline: Artificial Intelligence Ethics, Psychology, Sociology
The paper addresses the psychological, social, and ethical challenges of integrating AI into daily life and emphasizes the need to design AI systems that uphold human values and well-being.
Methods: The paper conducts an interdisciplinary review of existing research and literature to analyze the psychological, social, and ethical dimensions of AI deployment.
Key Findings: The impact of AI on human behavior, decision-making, and societal values.
DOI: https://doi.org/10.1089/cyber.2025.0202
Citations: 3
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Authors: J Li, E Huusko, NN Ahooie, M Kuutila
Year: 2025
Published in: ... Journal of Human ..., 2025 - Taylor & Francis
Institution: University of Oulu
Research Area: Social Media Credibility, Human-Computer Interaction (HCI) in Social Media, Crowdsourcing Research
Discipline: Human-Computer Interaction (HCI)
Credtwi, a browser plugin for assessing tweet credibility, revealed that perceived Twitter credibility declines with use and author verification status heavily influences perceived credibility.
Methods: A browser plugin was used for crowdsourced credibility assessment through participant questionnaires during a week-long field study.
Key Findings: Perceptions of online tweet credibility, factors affecting tweet credibility (e.g., verification status, bio), variations in credibility assessments across genders.
DOI: https://doi.org/10.1080/10447318.2025.2480885
Citations: 2
Sample Size: 150
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Authors: D OConnell, A Bautista
Year: 2025
Published in: ... Student Journal of ..., 2025 - journals.library.columbia.edu
Institution: University of Houston, Webster University
Research Area: Crowdsourcing Research Methodology, Human-Computer Interaction (HCI)
Discipline: Computational Social Science, Behavioral Research
Prolific outperforms MTurk in participant data quality and affordability for online survey-based research.
Methods: Data from participants recruited via MTurk and Prolific were analyzed for cost, attention measures, participation duration, and internal consistency.
Key Findings: Comparison of data quality and cost-effectiveness between MTurk and Prolific for online survey recruitment.
Citations: 1
Sample Size: 699
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Authors: A Qian, R Shaw, L Dabbish, J Suh, H Shen
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Carnegie Mellon University, University of Pittsburgh, University of Utah, Yale School of Medicine, Yale University
Research Area: Responsible AI, Content Moderation, Risk Disclosure, Worker Well-being in Human-Computer Interaction (HCI).
Discipline: Computational Social Science, Human-Computer Interaction (HCI)
The paper examines how task designers approach well-being risk disclosure in Responsible AI (RAI) content work, highlighting a need for better frameworks to communicate such risks effectively.
Methods: Interviews were conducted with 23 task designers from academic and industry sectors to gather insights on risk recognition, interpretation, and communication practices.
Key Findings: How task designers recognize, interpret, and communicate well-being risks in RAI content work.
Citations: 1
Sample Size: 23
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Authors: J Szczuka, L Mühl, P Ebner, S Dubé
Year: 2025
Published in: ArXiv
Institution: University of Duisburg-Essen
Research Area: Human-Computer Interaction (HCI), Social Psychology, Interpersonal Relationships with AI, LLM Evaluation
Discipline: Social Science
Participants rated AI-generated dating profile responses equally as human-like in terms of closeness and romantic interest, challenging assumptions about authenticity in online communication.
Methods: Participants evaluated 10 AI-generated responses to an interpersonal closeness task in a matchmaking scenario, without knowing the responses were AI-generated.
Key Findings: Impact of perceived response source (human vs AI) on interpersonal closeness and romantic interest; influence of perceived quality and human-likeness.
Sample Size: 307
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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
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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.
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Authors: DA Albert, D Smilek
Year: 2024
Published in: Scientific Reports, 2023 - nature.com
Institution: University of Waterloo, University of Waterloo
Research Area: Crowdsourcing Research Methods, Behavioral Science, Human-Computer Interaction (HCI)
Discipline: Psychological Science
Prolific participants exhibited lower levels of attentional disengagement compared to MTurk participants, with risk conditions and platform traits influencing task performance and disengagement.
Methods: Participants from Prolific and MTurk completed an attention task with varying error risk levels (high vs. low), and attentional disengagement was measured using task performance, self-reported mind wandering, and multitasking.
Key Findings: Attentional disengagement through task performance, mind wandering, and multitasking under different risk conditions across two recruitment platforms (Prolific and MTurk).
Citations: 150
Sample Size: 80
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Authors: M Reis, F Reis, W Kunde
Year: 2024
Published in: Nature Medicine, 2024 - nature.com
Institution: University of Cambridge, Julius Maximilians Universität
Research Area: AI in Healthcare, Medical Ethics, Cognitive Psychology, Human-Computer Interaction (HCI) in Medicine
Discipline: AI in Healthcare, Medical Ethics, Cognitive Psychology
The study found that medical advice labeled as being sourced from AI (or AI supervised by humans) is perceived as less reliable and empathetic compared to advice labeled as originating solely from a human physician, resulting in reduced willingness to follow such advice.
Methods: Two preregistered studies were conducted where participants were presented with identical medical advice scenarios but with manipulated labels for the advice source ('AI', 'human physician', 'human+AI').
Key Findings: Participants' perceptions of reliability, empathy, and willingness to follow medical advice based on the perceived source.
Citations: 78
Sample Size: 2280
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Authors: PW Mirowski, J Love, K Mathewson, S Mohamed
Year: 2024
Published in: ArXiv
Institution: Google DeepMind, Google
Research Area: AI Creativity, Humor Generation, Human-Computer Interaction (HCI)
Discipline: Artificial Intelligence
Professional comedians found LLMs insufficient as creativity support tools for comedy, citing bias, bland output, and reinforcement of hegemonic viewpoints.
Methods: Workshops conducted with professional comedians combining comedy writing sessions using LLMs, a Creativity Support Index questionnaire, and focus groups discussing their experiences and ethical concerns.
Key Findings: Effectiveness of LLMs as creativity support tools for comedy writing, ethical concerns (bias, censorship, copyright), and value alignment in AI outputs.
Citations: 52
Sample Size: 20
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Authors: AYJ Ha, J Passananti, R Bhaskar, S Shan
Year: 2024
Published in: Proceedings of the ..., 2024 - dl.acm.org
Institution: University of California Santa Barbara, The University of Chicago, Institute of Education, University College London
Research Area: Human-Computer Interaction (HCI), Generative AI, Digital Forensics
Discipline: Human-Computer Interaction (HCI), Generative AI, Digital Forensics
The paper investigates the effectiveness of different approaches, including both human and automated detectors, in distinguishing human art from AI-generated images, finding that a combination of methods offers the best performance despite persistent weaknesses.
Methods: Comparison of human art across 7 styles with AI-generated images from 5 generative models, assessed using 5 automated detectors and 3 human groups (crowdworkers, professional artists, expert artists).
Key Findings: Detection accuracy and robustness of human and automated methods in identifying AI-generated images under benign and adversarial conditions.
DOI: 10.1145/3658644.3670306
Citations: 52
Sample Size: 3993