Using Artificial Intelligence to Better Understand Human Intelligence

Abstract

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.

Keywords

artificial intelligence research methods experimental psychology intelligence reasoning evidence evaluation

Get full access to this article

View all access options for this article.

References

Ayyamperumal

S. G.

(2024). Current state of LLM risks and AI guardrails. arXiv. https://doi.org/10.48550/arXiv.2406.12934

Boissin

Costello

T. H.

Spinoza-Martín

Rand

D. G.

Pennycook

(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

Chen

Liu

Guo

Zhang

(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

Clark

C. J.

Winegard

B. 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

Costello

T. H.

Pennycook

Rand

D. G.

(2024). Durably reducing conspiracy beliefs through dialogues with AI. Science, 385(6714), Article eadq1814. https://doi.org/10.1126/science.adq1814

Costello

T. H.

Pennycook

Rand

D. G.

(2025). Just the facts: How dialogues with AI reduce conspiracy beliefs. PsyArXiv. https://doi.org/10.31234/osf.io/h7n8u_v1

Czarnek

Orchinik

Lin

Costello

Pennycook

Rand

(2025). Addressing climate change skepticism and inaction using human-AI dialogues. PsyArXiv. https://doi.org/10.31234/osf.io/mqcwj_v1

Delgado-Chaves

F. M.

Jennings

M. J.

Atalaia

Wolff

Horvath

Mamdouh

Z. M.

Baumbach

(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

Ditto

P. H.

Celniker

J. B.

Siddiqi

S. S.

Güngör

Relihan

D. P.

(2024). Partisan bias in political judgment. Annual Review of Psychology, 76, 717–740. https://doi.org/10.1146/annurev-psych-030424-122723

10.

Evans

J. St. B. T

. (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278. https://doi.org/10.1146/annurev.psych.59.103006.093629

11.

Frankish

Evans

J. S. B. T

. (2009). The duality of mind: An historical perspective. In Evans

J. S. B. T.

Frankish

(Eds.), In two minds: Dual processes and beyond (pp. 1–29). Oxford University Press.

12.

Haidt

(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.

Kahan

D. M.

(2013). Ideology, motivated reasoning, and cognitive reflection. Judgment and Decision Making, 8(4), 407–424. https://doi.org/10.2139/ssrn.2182588

14.

Kahneman

(2011). Thinking, fast and slow. Farrar, Straus and Giroux.

15.

Karjus

(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.

Kowal

Timm

Godbout

J.-F.

Costello

Arechar

A. A.

Pennycook

Rand

Gleave

Pelrine

(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

17.

Kunda

(1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480–498. https://doi.org/10.1037/0033-2909.108.3.480

18.

Myung

J. I.

Cavagnaro

D. R.

Pitt

M. 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’Mahony

Murphy

Linehan

(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.

Pennycook

(2023). A framework for understanding reasoning errors: From fake news to climate change and beyond. Advances in Experimental Social Psychology, 67, 131–208.

21.

Rathje

Mirea

D.-M.

Sucholutsky

Marjieh

Robertson

C. E.

Van Bavel

J. 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.

Salah

Al Halbusi

Abdelfattah

(2023). May the 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.

Simon

H. 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.

Simonsohn

Montealegre

Evangelidis

(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.

Stanovich

K. E.

(2005). The robot’s rebellion: Finding meaning in the age of Darwin. Chicago University Press.

26.

Stasielowicz

(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.

Sternberg

R. J.

(2018). Theories of intelligence. In Pfeiffer

S. I.

Shaunessy-Dedrick

Foley-Nicpon

(Eds.), APA handbook of giftedness and talent (pp. 145–161). American Psychological Association.

28.

Sunstein

C. R.

Vermeule

(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.

Costello

Schwartz

Niccolai

Pennycook

Rand

(2025). Personalized dialogues with AI effectively address parents’ concerns about HPV vaccination. PsyArXiv. https://doi.org/10.31234/osf.io/gv52j_v1