Can AI language models replace human participants?

Out of One, Many: Using Language Models to Simulate Human Samples

Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023) · Political Analysis · doi:10.1017/pan.2023.2 · Cited by 783 · see citing works

Untitled work

(2019) · Cited by 771 · see citing works

Using cognitive psychology to understand GPT-3

Binz, M., & Schulz, E. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218523120 · Cited by 559 · see citing works

Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?

Horton, J., Filippas, A., & Manning, B. (2023) · National Bureau of Economic Research · doi:10.3386/w31122 · Cited by 329 · see citing works

Moral foundations vignettes: a standardized stimulus database of scenarios based on moral foundations theory

Clifford, S., Iyengar, V., Cabeza, R., & Sinnott-Armstrong, W. (2015) · Behavior Research Methods · doi:10.3758/s13428-014-0551-2 · Cited by 328 · see citing works

Large pre-trained language models contain human-like biases of what is right and wrong to do

Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00458-8 · Cited by 291 · see citing works

Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

Aher, G., Arriaga, R. I., & Kalai, A. T. (2022) · arXiv · doi:10.48550/arxiv.2208.10264 · Cited by 161 · see citing works

Language Models and Cognitive Automation for Economic ResearchPreprint

Korinek, A. (2023) · Social Science Research Network · doi:10.2139/ssrn.4356243 · Cited by 129 · see citing works

Language Models and Cognitive Automation for Economic Research

Korinek, A. (2023) · doi:10.3386/w30957 · Cited by 129 · see citing works

Whose Opinions Do Language Models Reflect?

Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., & Hashimoto, T. (2023) · arXiv · doi:10.48550/arxiv.2303.17548 · Cited by 114 · see citing works

Do Audiences Judge the Morality of Characters Relativistically? How Interdependence Affects Perceptions of Characters’ Temporal Moral Descent

Grizzard, M., Matthews, N. L., Francemone, C. J., & Fitzgerald, K. S. (2021) · Human Communication Research · doi:10.1093/hcr/hqab011 · Cited by 41 · see citing works

The moral repetition effect: Bad deeds seem less unethical when repeatedly encountered.

Effron, D. A. (2022) · Journal of Experimental Psychology General · doi:10.1037/xge0001214 · Cited by 35 · see citing works

Off-Duty Deviance in the Eye of the Beholder: Implications of Moral Foundations Theory in the Age of Social Media

Cook, W. L., & Kuhn, K. M. (2020) · Journal of Business Ethics · doi:10.1007/s10551-020-04501-9 · Cited by 26 · see citing works

The Psychology of Robots and Artificial Intelligence

Gray, K. J., Yam, K. C., Eng, A., Dillion, D., & Waytz, A. G. (2025) · doi:10.70400/ikvm1257 · Cited by 15 · see citing works

Impression formation stimuli: A corpus of behavior statements rated on morality, competence, informativeness, and believability

Mickelberg, A. J., Walker, B., Ecker, U. K. H., Howe, P. D. L., Perfors, A., & Fay, N. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269393 · Cited by 12 · see citing works

Artificial intelligence and illusions of understanding in scientific research

Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707 · see citing works

Can Generative AI improve social science?

Bail, C. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2314021121 · Cited by 369 · see citing works

GPT is an effective tool for multilingual psychological text analysis

Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308950121 · Cited by 297 · see citing works

Synthetic Replacements for Human Survey Data? The Perils of Large Language Models

Bisbee, J., Clinton, J. D., Dorff, C., Kenkel, B., & Larson, J. M. (2024) · Political Analysis · doi:10.1017/pan.2024.5 · Cited by 209 · see citing works

Generative AI in Higher Education: The ChatGPT Effect

Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203 · see citing works

Which Humans?

Atari, M., Xue, M. J., Park, P. S., Blasi, D. E., & Henrich, J. (2023) · doi:10.31234/osf.io/5b26t · Cited by 151 · see citing works

Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines

Sarstedt, M., Adler, S. J., Rau, L., & Schmitt, B. (2024) · Psychology & Marketing · doi:10.1002/mar.21982 · Cited by 149 · see citing works

AI for social science and social science of AI: A survey

Xu, R. F., Sun, Y., Ren, M., Guo, S., Pan, R., Lin, H., Sun, L., & Han, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103665 · Cited by 149 · see citing works

How large language models can reshape collective intelligence

Burton, J. W., Lopez-Lopez, E., Hechtlinger, S., Rahwan, Z., Aeschbach, S., Bakker, M. A., Becker, J. A., Berditchevskaia, A., Berger, J., Brinkmann, L. U. L., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01959-9 · Cited by 124 · see citing works

Large language models that replace human participants can harmfully misportray and flatten identity groups

Wang, A., Morgenstern, J., & Dickerson, J. P. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00986-z · Cited by 192 · see citing works

Frontiers: Can Large Language Models Capture Human Preferences?

Goli, A., & Singh, A. (2024) · Marketing Science · doi:10.1287/mksc.2023.0306 · Cited by 115 · see citing works

A foundation model to predict and capture human cognition

Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., et al. (2025) · Nature · doi:10.1038/s41586-025-09215-4 · Cited by 119 · see citing works

Perils and opportunities in using large language models in psychological research

Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae245 · Cited by 111 · see citing works

Reclaiming AI as a Theoretical Tool for Cognitive Science

van Rooij, I., Guest, O., Adolfi, F., de Haan, R., Kolokolova, A., & Rich, P. (2024) · Computational Brain & Behavior · doi:10.1007/s42113-024-00217-5 · Cited by 103 · see citing works

Generative AI in Marketing and Principles for Ethical Design and Deployment

Hermann, E., & Puntoni, S. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241309874 · Cited by 93 · see citing works

The Future of Marketing and Marketing Education

Grewal, D., Guha, A., Satornino, C. B., & Becker, M. (2024) · Journal of Marketing Education · doi:10.1177/02734753241269838 · Cited by 83 · see citing works

Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design

Tjuatja, L., Chen, V., Wu, T., Talwalkwar, A., & Neubig, G. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00685 · Cited by 100 · see citing works

Performance and biases of large language models in public opinion simulation.

Qu, Y., & Wang, J. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03609-x · Cited by 79 · see citing works

Diminished diversity-of-thought in a standard large language model

Park, P. S., Schoenegger, P., & Zhu, C. (2024) · Behavior Research Methods · doi:10.3758/s13428-023-02307-x · Cited by 83 · see citing works

How should the advancement of large language models affect the practice of science?

Binz, M., Alaniz, S., Roskies, A., Aczel, B., Bergstrom, C. T., Allen, C., Schad, D., Wulff, D., West, J. D., Zhang, Q., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401227121 · Cited by 74 · see citing works

Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks

Veselovsky, V., Ribeiro, M. H., & West, R. (2023) · arXiv · doi:10.48550/arxiv.2306.07899 · Cited by 73 · see citing works

Friend or foe? Exploring the implications of large language models on the science system

Fecher, B., Hebing, M., Laufer, M., Pohle, J., & Sofsky, F. (2025) · AI & SOCIETY · doi:10.1007/s00146-023-01791-1 · Cited by 78 · see citing works

Exploring the psychology of LLMs’ moral and legal reasoning

Almeida, G. F. C. F., Nunes, J. L., Engelmann, N., Wiegmann, A., & Araújo, M. D. (2024) · Artificial Intelligence · doi:10.1016/j.artint.2024.104145 · Cited by 70 · see citing works

Do large language models have a legal duty to tell the truth?

Wachter, S., Mittelstadt, B., & Russell, C. (2024) · Royal Society Open Science · doi:10.1098/rsos.240197 · Cited by 85 · see citing works

Generative agent‐based modeling: an introduction and tutorial

Ghaffarzadegan, N., Majumdar, A., Williams, R., & Hosseinichimeh, N. (2024) · System Dynamics Review · doi:10.1002/sdr.1761 · Cited by 88 · see citing works

Large language models predict human sensory judgments across six modalities

Marjieh, R., Sucholutsky, I., van Rijn, P., Jacoby, N., & Griffiths, T. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-72071-1 · Cited by 73 · see citing works

Exploring the frontiers of LLMs in psychological applications: a comprehensive review

Ke, L., Tong, S., Cheng, P., & Peng, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11297-5 · Cited by 92 · see citing works

Can Generative AI Improve Social Science?

Bail, C. A. (2023) · doi:10.31235/osf.io/rwtzs · Cited by 369 · see citing works

ChatGPT as Research Scientist: Probing GPT’s capabilities as a Research Librarian, Research Ethicist, Data Generator, and Data Predictor

Lehr, S. A., Caliskan, A., Liyanage, S., & Banaji, M. R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404328121 · Cited by 66 · see citing works

Generative Artificial Intelligence in Qualitative Data Analysis: Analyzing—Or Just Chatting?

Nguyen, D. C., & Welch, C. (2025) · Organizational Research Methods · doi:10.1177/10944281251377154 · Cited by 70 · see citing works

Take caution in using LLMs as human surrogates

Gao, Y., Lee, D., Burtch, G., & Fazelpour, S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2501660122 · Cited by 52 · see citing works

Knowledge of cultural moral norms in large language models

Ramezani, A., & Xu, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.26 · Cited by 54 · see citing works

GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations

Kirshner, S. N. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103580 · Cited by 54 · see citing works

AI language models cannot replace human research participants

Harding, J., D’Alessandro, W., Laskowski, N. G., & Long, R. (2023) · AI & Society · doi:10.1007/s00146-023-01725-x · Cited by 53 · see citing works

Large language models show amplified cognitive biases in moral decision-making

Cheung, V., Maier, M., & Lieder, F. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412015122 · Cited by 53 · see citing works

Automating the practice of science: Opportunities, challenges, and implications

Musslick, S., Bartlett, L., Chandramouli, S., Dubova, M., Gobet, F. R., Griffiths, T. L., Hullman, J. R., King, R. D., Kutz, J. N., Lucas, C. G., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2401238121 · Cited by 56 · see citing works

AI language model rivals expert ethicist in perceived moral expertise

Dillion, D., Mondal, D., Tandon, N., & Gray, K. J. (2025) · Scientific Reports · doi:10.1038/s41598-025-86510-0 · Cited by 54 · see citing works

Attributions toward artificial agents in a modified Moral Turing Test

Aharoni, E., Fernandes, S., Brady, D. J., Alexander, C., Criner, M., Queen, K., Rando, J., Nahmias, E., & Crespo, V. (2024) · Scientific Reports · doi:10.1038/s41598-024-58087-7 · Cited by 65 · see citing works

(Ir)rationality and cognitive biases in large language models

Macmillan-Scott, O., & Musolesi, M. (2024) · Royal Society Open Science · doi:10.1098/rsos.240255 · Cited by 55 · see citing works

The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference PerspectivePreprint

Gui, G., & Toubia, O. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4650172 · Cited by 48 · see citing works

Systematic Biases in LLM Simulations of Debates

Taubenfeld, A., Dover, Y., Reichart, R., & Goldstein, A. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.16 · Cited by 47 · see citing works

The good, the bad, and the GPT: Reviewing the impact of generative artificial intelligence on psychology

Salah, M., Abdelfattah, F., & Halbusi, H. A. (2024) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2024.101872 · Cited by 47 · see citing works

Large language models display human-like social desirability biases in Big Five personality surveys

Salecha, A., Ireland, M. E., Subrahmanya, S., Sedoc, J., Ungar, L. H., & Eichstaedt, J. C. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae533 · Cited by 55 · see citing works

A Reasoning and Value Alignment Test to Assess Advanced GPT Reasoning

Mcintosh, T. R., Liu, T., Susnjak, T., Watters, P., & Halgamuge, M. N. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3670691 · Cited by 42 · see citing works

Can large language models help augment English psycholinguistic datasets?

Trott, S. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02337-z · Cited by 45 · see citing works

Cognitive Biases and Artificial Intelligence

Wang, J., & Redelmeier, D. A. (2024) · NEJM AI · doi:10.1056/aics2400639 · Cited by 43 · see citing works

A large-scale replication of scenario-based experiments in psychology and management using large language models

Cui, Z., Li, N., & Zhou, H. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00840-7 · Cited by 46 · see citing works

The homogenizing effect of large language models on human expression and thought

Sourati, Z., Ziabari, A. S., & Dehghani, M. (2026) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2026.01.003 · Cited by 62 · see citing works

Start Generating: Harnessing Generative Artificial Intelligence for Sociological Research

Davidson, T. (2024) · Socius Sociological Research for a Dynamic World · doi:10.1177/23780231241259651 · Cited by 51 · see citing works

Studying and improving reasoning in humans and machines

Yax, N., Anlló, H., & Palminteri, S. (2024) · Communications Psychology · doi:10.1038/s44271-024-00091-8 · Cited by 40 · see citing works

A Primer for Evaluating Large Language Models in Social-Science Research

Abdurahman, S., Ziabari, A. S., Moore, A. K., Bartels, D. M., & Dehghani, M. (2025) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251325174 · Cited by 51 · see citing works

Prompt architecture induces methodological artifacts in large language models

Brucks, M. S., & Toubia, O. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319159 · Cited by 42 · see citing works

Synthetic Replacements for Human Survey Data? The Perils of Large Language Models

Bisbee, J., Clinton, J. D., Dorff, C., Kenkel, B., & Larson, J. (2023) · doi:10.31235/osf.io/5ecfa · Cited by 213 · see citing works

Database Report: Twin-2K-500: A Data Set for Building Digital Twins of over 2,000 People Based on Their Answers to over 500 Questions

Toubia, O., Gui, G., Peng, T., Merlau, D. J., Li, A., & Chen, H. (2025) · Marketing Science · doi:10.1287/mksc.2025.0262 · Cited by 39 · see citing works

Can AI serve as a substitute for human subjects in software engineering research?

Gerosa, M. A., Trinkenreich, B., Steinmacher, I., & Sarma, A. (2024) · Automated Software Engineering · doi:10.1007/s10515-023-00409-6 · Cited by 35 · see citing works

Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models

Bian, Y., Küster, D., Liu, H., & Krumhuber, E. G. (2023) · Sensors · doi:10.3390/s24010126 · Cited by 34 · see citing works

More or less than human? Evaluating the role of AI-as-participant in online qualitative research

Gibson, A. F., & Beattie, A. (2024) · Qualitative Research in Psychology · doi:10.1080/14780887.2024.2311427 · Cited by 31 · see citing works

Generative AI lacks the human creativity to achieve scientific discovery from scratch

Ding, A. W., & Li, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-93794-9 · Cited by 31 · see citing works

From text to map: a system dynamics bot for constructing causal loop diagrams

Hosseinichimeh, N., Majumdar, A., Williams, R., & Ghaffarzadegan, N. (2024) · System Dynamics Review · doi:10.1002/sdr.1782 · Cited by 30 · see citing works

Human-like object concept representations emerge naturally in multimodal large language models

Du, C., Fu, K., Wen, B., Sun, Y., Peng, J., Wei, W., Gao, Y., Wang, S., Zhang, C., Li, J., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01049-z · Cited by 30 · see citing works

Understanding AI-Generated Experiments in Tourism: Replications Using GPT Simulations

Xiong, X., Wong, I. A., Huang, G. I., & Peng, Y. (2024) · Journal of Travel Research · doi:10.1177/00472875241275945 · Cited by 30 · see citing works

Large Language Models based on historical text could offer informative tools for behavioral science

Varnum, M. E. W., Baumard, N., Atari, M., & Gray, K. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2407639121 · Cited by 30 · see citing works

Framework-based qualitative analysis of free responses of Large Language Models: Algorithmic fidelity

Amirova, A., Fteropoulli, T., Ahmed, N., Cowie, M., & Leibo, J. Z. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300024 · Cited by 47 · see citing works

Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained ModelPreprint

Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. Κ. (2023) · Preprints.org · doi:10.20944/preprints202307.2142.v2 · Cited by 29 · see citing works

Using cognitive psychology to understand GPT-like models needs to extend beyond human biases

Stella, M., Hills, T. T., & Kenett, Y. N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2312911120 · Cited by 27 · see citing works

Multi-Agent Collaboration Mechanisms: A Survey of LLMs

Tran, K.-T., Dao, D., Nguyen, M.-D., Pham, Q.-V., O'Sullivan, B., & Nguyen, H. D. (2025) · arXiv · doi:10.48550/arxiv.2501.06322 · Cited by 26 · see citing works

Theory of Mind in Large Language Models: Examining Performance of 11 State-of-the-Art models vs. Children Aged 7-10 on Advanced Tests

van Duijn, M., van Dijk, B., Kouwenhoven, T., de Valk, W., Spruit, M., & vanderPutten, P. (2023) · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) · doi:10.18653/v1/2023.conll-1.25 · Cited by 24 · see citing works

The use of synthetic data in tourism

Viglia, G., Adler, S. J., Miltgen, C. L., & Sarstedt, M. (2024) · Annals of Tourism Research · doi:10.1016/j.annals.2024.103819 · Cited by 23 · see citing works

Automatic recognition of second language speech-in-noise

Kim, S.-E., Chernyak, B. R., Seleznova, O., Keshet, J., Goldrick, M., & Bradlow, A. R. (2024) · JASA Express Letters · doi:10.1121/10.0024877 · Cited by 24 · see citing works

Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns

Bian, N., Lin, H., Liu, P., Lu, Y., Zhang, C.-P., He, B., Han, X., & Sun, L. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3476030 · Cited by 36 · see citing works

Will Intelligent Technologies Replace Humans in the Future? An Exploratory Study for Likelihood of Intelligent Technologies to Replace Humans in the Future

Eng, P. H., & Liu, R. L. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-61463-7_9 · Cited by 25 · see citing works

Machine Bias. How Do Generative Language Models Answer Opinion Polls?

Boelaert, J., Coavoux, S., Ollion, É., Petev, I. D., & Präg, P. (2025) · Sociological Methods & Research · doi:10.1177/00491241251330582 · Cited by 30 · see citing works

Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models

Wang, Z.-Y., Wang, D.-Q., Xu, Y., Zhou, L.-F., & Zhou, Y.-Q. (2024) · Journal of Chinese Political Science · doi:10.1007/s11366-025-09917-6 · Cited by 23 · see citing works

An integrated model of semantics and control.

Giallanza, T., Campbell, D. I., Cohen, J., & Rogers, T. T. (2024) · Psychological Review · doi:10.1037/rev0000485 · Cited by 24 · see citing works

Moving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge

Brysbaert, M., Martínez, G., & Reviriego, P. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02561-7 · Cited by 22 · see citing works

Using Large Language Models for Qualitative Analysis can Introduce Serious Bias

Ashwin, J., Chhabra, A., & Rao, V. (2026) · Sociological Methods & Research · doi:10.1177/00491241251338246 · Cited by 22 · see citing works

Beyond WEIRD: Can synthetic survey participants substitute for humans in global policy research?

Shrestha, P., Krpan, D., Koaik, F., Schnider, R., Sayess, D., & Binbaz, M. S. (2024) · Behavioral Science & Policy · doi:10.1177/23794607241311793 · Cited by 21 · see citing works

Moral stereotyping in large language models

Zewail, A., Figueroa, A., Graham, J., & Atari, M. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2519941123 · Cited by 23 · see citing works

Large Language Models and the Wisdom of Small Crowds

Trott, S. (2024) · Open Mind · doi:10.1162/opmi_a_00144 · Cited by 22 · see citing works

CERN for AI: a theoretical framework for autonomous simulation-based artificial intelligence testing and alignment

Bojić, L., Cinelli, M., Culibrk, D. R., & Delibašić, B. (2024) · European Journal of Futures Research · doi:10.1186/s40309-024-00238-0 · Cited by 21 · see citing works

Llama 2: Early Adopters' Utilization of Meta's New Open-Source Pretrained ModelPreprint

Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. Κ. (2023) · Preprints.org · doi:10.20944/preprints202307.2142.v1 · Cited by 21 · see citing works

Advancing food safety behavior with AI: Innovations and opportunities in the food manufacturing sector

Wang, K., Mirosa, M., Hou, Y., & Bremer, P. J. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105050 · Cited by 20 · see citing works

A Survey of LLM Datasets: From Autoregressive Model to AI Chatbot

Du, F., Ma, X.-J., Yang, J., Liu, Y., Luo, C.-R., Wang, X., Jiang, H.-O., & Jing, X. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-3767-3 · Cited by 22 · see citing works

Six Fallacies in Substituting Large Language Models for Human Participants

Lin, Z. (2025) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251357566 · Cited by 20 · see citing works

Responsible AI and human collaboration in tourism management: Ethical considerations and identity disclosure

Huang, G. I., Zhang, C., Torres, W. C. Z., Xiong, X., & Li, H. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105315 · Cited by 19 · see citing works

Large language models can predict the results of social science experiments

Ashokkumar, A., Hewitt, L., Ghezae, I., & Willer, R. (2026) · Nature · doi:10.1038/s41586-026-10742-x · Cited by 25 · see citing works

Studying large language models as compression algorithms for human culture

Buttrick, N. R. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.01.001 · Cited by 19 · see citing works

Mobilizing New Sources of Data: Opportunities and Recommendations

Grégoire, D. A., Wal, A. L. J. T., Little, L. M., Bermiss, Y. S., Kotha, R. R., & Gruber, M. B. (2024) · Academy of Management Journal · doi:10.5465/amj.2024.4002 · Cited by 19 · see citing works

Large language models (LLMs) as agents for augmented democracy

Gudiño, J., Grandi, U., & Hidalgo, C. A. (2024) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2024.0100 · Cited by 19 · see citing works

Beauty or the Borg: Agentic artificial intelligence organizational socialization in synergistic Hybrid Transformative Dynamic Flows

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The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations

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Integration of cognitive tasks into artificial general intelligence test for large models

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AI-generated estimates of familiarity, concreteness, valence, and arousal for over 100,000 Spanish words

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Comparing human and synthetic data in service research: using augmented language models to study service failures and recoveries

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Recommending With, Not For: Co-Designing Recommender Systems for Social Good

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Demonstrations of the Potential of AI-based Political Issue Polling

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Artificial intelligence chatbots mimic human collective behaviour

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Theorizing with Large Language Models

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Toward accurate psychological simulations: Investigating LLMs’ responses to personality and cultural variables

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The impact of ChatGPT on human data collection: A case study involving typicality norming data

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Should large language models replace human participants?Preprint

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Large language models are still far from replacing human participantsPreprint

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Six Fallacies in Substituting Large Language Models for Human Participants

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Large Language Models as Linguistic Simulators and Cognitive Models in Human ResearchPreprint

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Large language models complement human participants through role simulationPreprint

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