Browse 24 peer-reviewed papers in Synthetic Data. Discover studies powered by high-quality human data from Prolific.
This page lists 24 peer-reviewed papers tagged with Synthetic Data in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: S Shekar, P Pataranutaporn, C Sarabu, GA Cecchi
Year: 2025
Published in: NEJM AI, 2025 - ai.nejm.org
Institution: MIT Media Lab, IBM Research, Stanford University, Massachusetts Institute of Technology
Research Area: AI Ethics, Healthcare, Patient Trust, Medical Misinformation
Discipline: Artificial Intelligence, Human-Computer Interaction, AI Ethics
This paper discusses a study by MIT researchers detailing patient trust in AI-generated medical advice, even when that advice is incorrect, raising concerns about misinformation in healthcare.
Citations: 19
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Authors: H Ju, S Aral
Year: 2025
Published in: arXiv preprint arXiv:2503.18238, 2025 - arxiv.org
Institution: Johns Hopkins Carey Business School, MIT Sloan School of Management
Research Area: Human-AI Collaboration, Teamwork, Organizational Productivity
Discipline: Human-AI Interaction
Collaboration with AI agents increases productivity, reshapes communication patterns, and improves text quality while human teams excel in image quality; AI requires fine-tuning for multimodal workflows.
Methods: Large-scale randomized controlled trials using Pairit platform with human-human and human-AI teams performing collaborative marketing tasks.
Key Findings: Productivity, communication patterns, workflow processes, ad quality (text and image), and ad performance metrics.
DOI: https://doi.org/10.48550/arXiv.2503.18238
Citations: 14
Sample Size: 2310
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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
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: 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
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: S Carney, I Riveros, S Tully
Year: 2025
Published in: Available at SSRN 4988760, 2025 - papers.ssrn.com
Institution: University of Southern California
Research Area: Consumer Engagement with AI Disclosures, Social Media Marketing, Social Psychology
Discipline: Social Science
AI-generated content disclosures on social media reduce consumer engagement primarily due to a decrease in parasocial connections, as users perceive creators to exert less effort; signaling greater effort can mitigate this effect.
Methods: Analysis of TikTok engagement data following AIGC disclosure implementation, supplemented by six preregistered experiments.
Key Findings: Impact of AIGC disclosures on consumer engagement and the mediating role of parasocial connections.
Citations: 6
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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
Discipline: Human-Computer Interaction
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: H Shakeeb, C Conrad
Year: 2025
Published in: 2025 - aisel.aisnet.org
Institution: Dalhousie University
Research Area: Artificial Intelligence, Political Communication, Media Trustworthiness, Cognitive Science, Autonomous Applications
Discipline: Artificial Intelligence, Cognitive Science
AI-generated audio in political communication is perceived as more trustworthy than image or video formats, but lower realism leads to skepticism.
Methods: An online experiment with participants assessing AI-generated political content in audio, video, and image formats; data analyzed using linear mixed effects analysis and NLP.
Key Findings: Impact of AI-generated media formats on trust and willingness to follow political recommendations, considering realism levels.
Citations: 1
Sample Size: 150
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Authors: N Schwitter
Year: 2025
Published in: Social Science Computer Review, 2025 - journals.sagepub.com
Institution: University of Lucerne
Research Area: Artificial Intelligence in Social Science Research Methods, Factorial Survey Experiments, Visual Vignettes Generation
Discipline: Social Science
This paper explores the use of generative AI for creating visual vignettes in factorial survey experiments, highlighting their potential to boost realism and engagement while addressing ethical and technical challenges.
Methods: Techniques for generating and selectively editing AI-generated images were demonstrated, and a pretest with human participants was conducted to evaluate perceptions and interpretations of the images.
Key Findings: Application of AI-generated visual vignettes in social science research and participant interpretation of these images.
Citations: 1
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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, 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: 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
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.
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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
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
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Authors: E Meguellati, S Civelli, L Han, A Bernstein
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Oregon Health Sciences University, Oregon University of California, Irvine, Han Institute, NYU School of Law, Bernstein Research
Research Area: Advertising, Persuasion Strategies, Human-AI Interaction in Content Generation
Discipline: Artificial Intelligence
LLM-generated advertisements achieved parity with human-written ads in personalization and demonstrated superiority in persuasion using psychological principles, outperforming human ads even when AI-origin detection impacted results.
Methods: Two-part study: First examined LLM personalization based on personality traits; second tested psychological persuasion principles using universal messages across authority, consensus, cognition, and scarcity.
Key Findings: Effectiveness of LLM-generated ads in personalization and persuasive storytelling compared to human-created ads.
Sample Size: 1200
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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
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Authors: Y Yin, N Jia, CJ Wakslak
Year: 2024
Published in: Proceedings of the National Academy of ..., 2024 - pnas.org
Institution: University of Southern California Los Angeles
Research Area: Human-AI Interaction, Social Perception of AI, Media Effects
Discipline: Social Science
AI responses make people feel more heard and are better at emotional support compared to humans, but labeling responses as AI diminishes this effect.
Methods: Experiment and follow-up study to assess recipient reactions to AI vs. human-generated responses and determine emotional support efficacy.
Key Findings: The degree to which recipients feel heard, emotion detection accuracy, and third-party ratings of emotional support quality.
DOI: https://doi.org/10.1073/pnas.2319112121
Citations: 201
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Authors: D Cooke, A Edwards, S Barkoff, K Kelly
Year: 2024
Published in: arXiv preprint arXiv:2403.16760, 2024 - arxiv.org
Institution: King’s College London, Center for Strategic and International Studies
Research Area: Detection of AI-Generated Multimedia, Misinformation, Human-Computer Interaction
Discipline: Human-Computer Interaction, Artificial Intelligence, Media Studies
People struggle to distinguish AI-generated media from authentic content, with detection accuracy averaging close to chance (50%), and performance declines in certain conditions like synthetic human faces, foreign languages, or heterogeneous media modalities.
Methods: Perceptual study evaluating participants' ability to identify authentic and synthetic images, audio, video, and audiovisual stimuli under various conditions.
Key Findings: Human ability to detect AI-generated media compared to authentic media, including factors like modality, content type, foreign languages, and participant demographics.
DOI: https://doi.org/10.48550/arXiv.2403.16760
Citations: 44
Sample Size: 1276
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Authors: A Berke, R Mahari, A Pentland, K Larson
Year: 2024
Published in: Proceedings of the ACM ..., 2024 - dl.acm.org
Institution: Stanford's CodeX Center, Harvard Law School, MIT Media Lab, Stanford Institute for Human-Centered AI, The Larson Institute, Massachusetts Institute of Technology, Stanford University
Research Area: Crowdsourcing, Transparency, Human-Computer Interaction (HCI) in Social Science Research
Discipline: Computational Social Science, Human-Computer Interaction
DOI: https://dl.acm.org/doi/abs/10.1145/3687005
Citations: 9
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Authors: JD Lomas, W van der Maden, S Bandyopadhyay
Year: 2024
Published in: Advanced Design ..., 2024 - Elsevier
Institution: Delft University of Technolog, Playpower Labs, Hong Kong Polytechnic University, Utrecht University
Research Area: AI Alignment, Affective Computing, Emotional Expression in Generative AI, Human Perception of AI Emotions
Discipline: Affective Computing, Artificial Intelligence, Human-Computer Interaction
This study evaluates how well generative AI systems (like DALL·E 2/3 and Stable Diffusion) can generate emotionally expressive content that aligns with how humans perceive those emotions, finding that model performance varies by emotion type and model, with implications for designing more emotionally aligned AI.
DOI: https://doi.org/10.1016/j.ijadr.2024.10.002
Citations: 5
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Authors: JD Lomas, W van der Maden
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - arxiv.org
Institution: Delft University of Technology, Microsoft Research
Research Area: Affective Computing, Human-AI Interaction, Image Generation
Discipline: Artificial Intelligence
DOI: https://doi.org/10.48550/arXiv.2405.18510
Citations: 5
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Authors: Memoona Aziz, Muhammad Umair Danish, Umair Rehman, Katarina Grolinger
Year: 2024
Published in: ArXiv
Institution: IEEE
Research Area: Computer Vision, AI-Generated Images, Image Quality Evaluation
Discipline: Artificial Intelligence, Computer Science
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Authors: D Testa, G Bonetta, R Bernardi
Year: 2024
Published in: Proceedings of the ..., 2025 - aclanthology.org
Institution: Università di Roma La Sapienza, Fondazione Bruno Kessler, University of Pisa
Research Area: Multimodal AI Assessment, Visual Language Models (VLMs), Video Understanding, Computational Linguistics
Discipline: Artificial Intelligence, Computational Linguistics
