A consistent pattern emerges from the history of psychology: Technological advances change the way that we understand ourselves. We argue that, in addition to various uses that are already common (e.g., qualitative coding), large language models can be integrated into survey software and act as a virtual research assistant that can generate tailored stimuli on the fly. This creates unprecedented flexibility in developing materials for psychological theory testing. We present an illustrative case study to show how a major lingering debate in the field—that is, whether people really change their mind according to evidence or, instead, rely on motivated reasoning—was pushed forward by using artificial intelligence (AI) to administer personalized experimental treatments. We discuss various potential uses of AI to test hypotheses in psychological science and argue that psychologists should seriously consider using AI to better understand human intelligence.
BoissinE.CostelloT. H.Spinoza-MartínD.RandD. G.PennycookG. (2025). Dialogues with large language models reduce conspiracy beliefs even when the AI is perceived as human. PNAS Nexus, 4(11), Article pgaf325. https://doi.org/10.1093/pnasnexus/pgaf325
3.
ChenD.LiuY.GuoY.ZhangY. (2024). The revolution of generative artificial intelligence in psychology: The interweaving of behavior, consciousness, and ethics. Acta Psychologica, 251, Article 104593. https://doi.org/10.1016/j.actpsy.2024.104593
4.
ClarkC. J.WinegardB. M. (2020). Tribalism in war and peace: The nature and evolution of ideological epistemology and its significance for modern social science. Psychological Inquiry, 31(1), 1–22. https://doi.org/10.1080/1047840X.2020.1721233
5.
CostelloT. H.PennycookG.RandD. G. (2024). Durably reducing conspiracy beliefs through dialogues with AI. Science, 385(6714), Article eadq1814. https://doi.org/10.1126/science.adq1814
CzarnekG.OrchinikR.LinH.XuH.CostelloT.PennycookG.RandD. (2025). Addressing climate change skepticism and inaction using human-AI dialogues. PsyArXiv. https://doi.org/10.31234/osf.io/mqcwj_v1
8.
Delgado-ChavesF. M.JenningsM. J.AtalaiaA.WolffJ.HorvathR.MamdouhZ. M.BaumbachJ.BaumbachL. (2025). Transforming literature screening: The emerging role of large language models in systematic reviews. Proceedings of the National Academy of Sciences, USA, 122(2), Article e2411962122. https://doi.org/10.1073/pnas.2411962122
FrankishK.EvansJ. S. B. T. (2009). The duality of mind: An historical perspective. In EvansJ. S. B. T.FrankishK. (Eds.), In two minds: Dual processes and beyond (pp. 1–29). Oxford University Press.
12.
HaidtJ. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral judgment. Psychological Review, 108(4), 814–834. https://doi.org/10.1037//0033-295X
13.
KahanD. M. (2013). Ideology, motivated reasoning, and cognitive reflection. Judgment and Decision Making, 8(4), 407–424. https://doi.org/10.2139/ssrn.2182588
14.
KahnemanD. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
15.
KarjusA. (2025). Machine-assisted quantitizing designs: Augmenting humanities and social sciences with artificial intelligence. Humanities and Social Sciences Communications, 12, Article 277. https://doi.org/10.1057/s41599-025-04503-w
16.
KowalM.TimmJ.GodboutJ.-F.CostelloT.ArecharA. A.PennycookG.RandD.GleaveA.PelrineK. (2025). It’s the thought that counts: Evaluating the attempts of frontier LLMs to persuade on harmful topics. arXiv. https://doi.org/10.48550/arXiv.2506.02873
MyungJ. I.CavagnaroD. R.PittM. A. (2013). A tutorial on adaptive design optimization. Journal of Mathematical Psychology, 57(3), 53–67. https://doi.org/10.1016/j.jmp.2013.05.005
19.
O’MahonyC.MurphyG.LinehanC. (2024). True discernment or blind scepticism? Comparing the effectiveness of four conspiracy belief interventions. Advances.in/Psychology, 2, e215691. https://doi.org/10.56296/aip00030
20.
PennycookG. (2023). A framework for understanding reasoning errors: From fake news to climate change and beyond. Advances in Experimental Social Psychology, 67, 131–208.
21.
RathjeS.MireaD.-M.SucholutskyI.MarjiehR.RobertsonC. E.Van BavelJ. J. (2024). GPT is an effective tool for multilingual psychological text analysis. Proceedings of the National Academy of Sciences, USA, 121(34), Article e2308950121. https://doi.org/10.1073/pnas.2308950121
22.
SalahM.Al HalbusiH.AbdelfattahF. (2023). Maythe force of text data analysis be with you: Unleashing the power of generative AI for social psychology research. Computers in Human Behavior: Artificial Humans, 1(2), Article 100006. https://doi.org/10.1016/j.chbah.2023.100006
23.
SimonH. A. (1981). Studying human intelligence by creating artificial intelligence: When considered as a physical symbol system, the human brain can be fruitfully studied by computer simulation of its processes. American Scientist, 69(3), 300–309.
24.
SimonsohnU.MontealegreA.EvangelidisI. (2025). Stimulus sampling reimagined: Designing experiments with mix-and-match, analyzing results with stimulus plots. Journal of Personality and Social Psychology, 129(1), 71–90. https://doi.org/10.1037/pspa0000449
25.
StanovichK. E. (2005). The robot’s rebellion: Finding meaning in the age of Darwin. Chicago University Press.
26.
StasielowiczL. (2025). The effectiveness of interventions addressing conspiracy beliefs: A meta-analysis. European Journal of Social Psychology. Advance online publication. https://doi.org/10.1002/ejsp.70041
27.
SternbergR. J. (2018). Theories of intelligence. In PfeifferS. I.Shaunessy-DedrickE.Foley-NicponM. (Eds.), APA handbook of giftedness and talent (pp. 145–161). American Psychological Association.
28.
SunsteinC. R.VermeuleA. (2009). Symposium on conspiracy theories: Conspiracy theories: Causes and cures. Journal of Political Philosophy, 17(2), 202–227. https://doi.org/10.1111/j.1467-9760.2008.00325.x
29.
XuH.CostelloT.SchwartzJ.NiccolaiL.PennycookG.RandD. (2025). Personalized dialogues with AI effectively address parents’ concerns about HPV vaccination. PsyArXiv. https://doi.org/10.31234/osf.io/gv52j_v1
