LLMs Amplify Gendered Empathy Stereotypes and Influence Major and Career Recommendations*

doi:10.3724/SP.J.1041.2026.0399

Abstract

Abstract:

Large language models (LLMs) are increasingly deployed in highly sensitive domains such as education and career guidance, raising concerns about their potential to reproduce and amplify social biases. The present research examined whether LLMs exhibit gendered empathy stereotypes—specifically, the belief that “women are more empathetic than men”—and whether such stereotypes influence downstream recommendations. Three studies were conducted. Study 1 compared LLMs with human participants and found that across six leading LLMs, gendered empathy stereotypes were significantly stronger than those observed in humans across three facets of empathy: emotional empathy, attention to others’ feelings, and behavioral empathy. Study 2 manipulated input language (Chinese vs. English) and gender-identity priming (male vs. female), demonstrating that English prompts and female priming elicited stronger gendered empathy stereotypes. Study 3 focused on major and career recommendation tasks and revealed that LLMs systematically recommended high-empathy majors and professions to women, while directing men toward low-empathy fields. Together, these findings indicate that LLMs exhibit pronounced gendered empathy stereotypes, that these biases vary across input context, and that they can transfer into real-world recommendation scenarios. This research offers theoretical insights into bias formation in LLMs and provides practical implications for improving fairness in AI systems used in educational and career guidance.

Key words: large language models, gender stereotypes, empathy, AI recommendations, human-computer interaction

DAI Yiqing, MA Xinming, WU Zhen. (2026). LLMs Amplify Gendered Empathy Stereotypes and Influence Major and Career Recommendations^*. Acta Psychologica Sinica, 58(3), 399-415.

Figures/Tables 25

References 58

[1]	Acerbi A., & Stubbersfield J. M. (2023). Large language models show human-like content biases in transmission chain experiments. Proceedings of the National Academy of Sciences, 120(44), Article e2313790120. https://doi.org/10.1073/pnas.2313790120
[2]	Bai X., Wang A., Sucholutsky I., & Griffiths T. L. (2025). Explicitly unbiased large language models still form biased associations. Proceedings of the National Academy of Sciences, 122(8), Article e2416228122. https://doi.org/10.1073/pnas.2416228122
[3]	Bates D., Mächler M., Bolker B., & Walker S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1-48.
[4]	Block K., Croft A., & Schmader T. (2018). Worth less?: Why men (and women) devalue care-oriented careers. Frontiers in Psychology, 9, Article 1353. https://doi.org/10.3389/fpsyg.2018.01353
[5]	Bolukbasi T., Chang K. W., Zou J. Y., Saligrama V., & Kalai A. T. (2016). Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. arXiv preprint arXiv:1607.06520.
[6]	Bridgstock R. (2009). The graduate attributes we’ve overlooked: Enhancing graduate employability through career management skills. Higher Education Research & Development, 28(1), 31-44.
[7]	Cai Y., Cao D., Guo R., Wen Y., Liu G., & Chen E. (2024, August). Locating and mitigating gender bias in large language models. In International Conference on Intelligent Computing (ICIC) (pp. 471-482). Tianjin, China.
[8]	Chaturvedi S., & Chaturvedi R. (2025). Who gets the callback? Generative AI and gender bias. arXiv preprint arXiv:2504.21400.
[9]	Chen Y., Liu T. X., Shan Y., & Zhong S. (2023). The emergence of economic rationality of GPT. Proceedings of the National Academy of Sciences, 120(51), Article e2316205120. https://doi.org/10.1073/pnas.2316205120
[10]	Cheng M., Durmus E., & Jurafsky D. (2023). Marked personas: Using natural language prompts to measure stereotypes in language models. arXiv preprint arXiv:2305.18189.
[11]	Cheung V., Maier M., & Lieder F. (2025). Large language models show amplified cognitive biases in moral decision-making. Proceedings of the National Academy of Sciences, 122(25), Article e2412015122. https://doi.org/10.1073/pnas.2412015122
[12]	Christov-Moore L., Simpson E. A., Coudé G., Grigaityte K., Iacoboni M., & Ferrari P. F. (2014). Empathy: Gender effects in brain and behavior. Neuroscience & Biobehavioral Reviews, 46, 604-627. doi: 10.1016/j.neubiorev.2014.09.001 URL
[13]	Croft A., Schmader T., & Block K. (2015). An underexamined inequality: Cultural and psychological barriers to men's engagement with communal roles. Personality and Social Psychology Review, 19(4), 343-370. doi: 10.1177/1088868314564789 pmid: 25576312
[14]	Dastin J. (2022). Amazon scraps secret AI recruiting tool that showed bias against women. In K. Martin (Ed.), Ethics of data and analytics (pp. 296-299). Auerbach Publications.
[15]	Decety J. (2010). The neurodevelopment of empathy in humans. Developmental Neuroscience, 32(4), 257-267. doi: 10.1159/000317771 pmid: 20805682
[16]	De Waal F. B. M. (2008). Putting the altruism back into altruism: The evolution of empathy. Annual Review Psychology, 59, 279-300. doi: 10.1146/psych.2008.59.issue-1 URL
[17]	Dong W., Zhunis A., Jeong D., Chin H., Han J., & Cha M. (2024). Persona setting pitfall: Persistent outgroup biases in large language models arising from social identity adoption. arXiv preprint arXiv:2409.03843.
[18]	Eagly A. H., & Koenig A. M. (2021). The vicious cycle linking stereotypes and social roles. Current Directions in Psychological Science, 30(4), 343-350. doi: 10.1177/09637214211013775 URL
[19]	Eagly A. H., & Steffen V. J. (1984). Gender stereotypes stem from the distribution of women and men into social roles. Journal of Personality and Social Psychology, 46(4), 735-754. doi: 10.1037/0022-3514.46.4.735 URL
[20]	Eagly, A. H., & Wood W. (2012). Social role theory. In P. A. M. Van Lange, A. W. Kruglanski, & (Eds.), Handbook of theories of social psychology (Vol. 2, pp. 458-476). Sage Publications Ltd.
[21]	Eccles J. (2011). Gendered educational and occupational choices: Applying the Eccles et al. model of achievement-related choices. International Journal of Behavioral Development, 35(3), 195-201. doi: 10.1177/0165025411398185 URL
[22]	Ferrara E. (2023). Should ChatGPT be biased? Challenges and risks of bias in large language models. First Monday, 28(11).
[23]	Glickman M., & Sharot T. (2025). How human-AI feedback loops alter human perceptual, emotional and social judgements. Nature Human Behaviour, 9(2), 345-359. doi: 10.1038/s41562-024-02077-2 pmid: 39695250
[24]	Gross N. (2023). What ChatGPT tells us about gender: A cautionary tale about performativity and gender biases in AI. Social Sciences, 12(8), Article 435. https://doi.org/10.3390/socsci12080435
[25]	Gupta S., Shrivastava V., Deshpande A., Kalyan A., Clark P., Sabharwal A., & Khot T. (2024). Bias runs deep: Implicit reasoning biases in persona-assigned LLMs. arXiv preprint arXiv:2311.04892.
[26]	Hoffman M. L. (1990). Empathy and justice motivation. Motivation and Emotion, 14(2), 151-172. doi: 10.1007/BF00991641 URL
[27]	Holland J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments. Psychological Assessment Resources.
[28]	Jung C. G. (1968). The archetypes and the collective unconscious. Routledge & Kegan Paul.
[29]	Kamas L., & Preston A. (2021). Empathy, gender, and prosocial behavior. Journal of Behavioral and Experimental Economics, 92, Article 101654. https://doi.org/10.1016/j.socec.2020.101654
[30]	Kaplan D. M., Palitsky R., Arconada Alvarez S. J., Pozzo N. S., Greenleaf M. N., Atkinson C. A., & Lam W. A. (2024). What’s in a name? Experimental evidence of gender bias in recommendation letters generated by ChatGPT. Journal of Medical Internet Research, 26, Article e51837. https://doi.org/10.2196/51837
[31]	Klein K. J., & Hodges S. D. (2001). Gender differences, motivation, and empathic accuracy: When it pays to understand. Personality and Social Psychology Bulletin, 27(6), 720-730. doi: 10.1177/0146167201276007 URL
[32]	Kong H., Ahn Y., Lee S., & Maeng Y. (2024). Gender bias in LLM- generated interview responses. arXiv preprint arXiv:2410.20739.
[33]	Kotek H., Dockum R., & Sun D. (2023, November). Gender bias and stereotypes in large language models. In Proceedings of the ACM collective intelligence conference (CI) (pp. 12-24). New York, United States.
[34]	Liu A., Diab M., & Fried D. (2024). Evaluating large language model biases in persona-steered generation. arXiv preprint arXiv:2405.20253.
[35]	Löffler C. S., & Greitemeyer T. (2023). Are women the more empathetic gender? The effects of gender role expectations. Current Psychology, 42(1), 220-231. doi: 10.1007/s12144-020-01260-8
[36]	Lu J. G., Song L. L., & Zhang L. D. (2025). Cultural tendencies in generative AI. Nature Human Behaviour. Advance online publication. https://doi.org/10.1038/s41562-025-02242-1
[37]	Martínez-Morato S., Feijoo-Cid M., Galbany-Estragués P., Fernández- Cano M. I., & Arreciado Marañón A. (2021). Emotion management and stereotypes about emotions among male nurses: A qualitative study. BMC Nursing, 20(1), Article 114. https://doi.org/10.1186/s12912-021-00641-z
[38]	Master A., Meltzoff A. N., & Cheryan S. (2021). Gender stereotypes about interests start early and cause gender disparities in computer science and engineering. Proceedings of the National Academy of Sciences, 118(48), Article e2100030118. https://doi.org/10.1073/pnas.2100030118
[39]	Murphy, M. C., & Taylor V. J. (2012). The role of situational cues in signaling and maintaining stereotype threat. In M. Inzlicht &(Eds.), Stereotype threat: Theory, process, and application (pp. 17-33). Oxford University Press.
[40]	National Bureau of Statistics of China. (2021). China labour statistical yearbook-2021. Beijing: China Statistic Press.
[41]	Noble S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York: New York University Press.
[42]	Olsson M. I. T., Froehlich L., Dorrough A. R., & Martiny S. E. (2021). The hers and his of prosociality across 10 countries. British Journal of Social Psychology, 60(4), 1330-1349. doi: 10.1111/bjso.12454 pmid: 33739472
[43]	Ostrow R., & Lopez A. (2025). LLMs reproduce stereotypes of sexual and gender minorities. arXiv preprint arXiv:2501.05926.
[44]	Plaza-del-Arco F. M., Curry A. C., Curry A., Abercrombie G., & Hovy D. (2024). Angry men, sad women: Large language models reflect gendered stereotypes in emotion attribution. arXiv preprint arXiv:2403.03121.
[45]	Prewitt-Freilino J. L., Caswell T. A., & Laakso E. K. (2012). The gendering of language: A comparison of gender equality in countries with gendered, natural gender, and genderless languages. Sex Roles, 66(3), 268-281. doi: 10.1007/s11199-011-0083-5 URL
[46]	Rieffe C., Ketelaar L., & Wiefferink C. H. (2010). Assessing empathy in young children: Construction and validation of an Empathy Questionnaire (EmQue). Personality and Individual Differences, 49(5), 362-367. doi: 10.1016/j.paid.2010.03.046 URL
[47]	Salinas A., Shah P., Huang Y., McCormack R., & Morstatter F. (2023, October). The unequal opportunities of large language models: Examining demographic biases in job recommendations by ChatGPT and LLaMA. In Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) (pp. 1-15). Boston, United States.
[48]	Sheng E., Chang K. W., Natarajan P., & Peng N. (2021). Societal biases in language generation: Progress and challenges. arXiv preprint arXiv:2105.04054.
[49]	Slobodin O., Samuha T., Hannona-Saban A., & Katz I. (2024). When boys and girls make their first career decisions: Exploring the role of gender and field in high school major choice. Social Psychology of Education, 27(5), 2455-2478. doi: 10.1007/s11218-024-09933-z
[50]	Smith M. S., Greaves L., & Mason D. (2025). Early careers survey 2025. Prospects Luminate, Jisc. https://graduatemarkettrends.cdn.prismic.io/graduatemarkettrends/aDb6SidWJ-7kSn7u_early-careers-survey-2025.pdf
[51]	Su R., Rounds J., & Armstrong P. I. (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135(6), 859-884. doi: 10.1037/a0017364 pmid: 19883140
[52]	Thomas G., & Maio G. R. (2008). Man, I feel like a woman: When and how gender-role motivation helps mind-reading. Journal of Personality and Social Psychology, 95(5), 1165-1179. doi: 10.1037/a0013067 pmid: 18954200
[53]	Torres N., Ulloa C., Araya I., Ayala M., & Jara S. (2024, October). Injecting bias through prompts: Analyzing the influence of language on LLMs. In 2024 43rd International Conference of the Chilean Computer Science Society (SCCC) (pp. 1-8). Temuco, Chile.
[54]	United Nations Educational, Scientific and Cultural Organization & International Research Centre on Artificial Intelligence. (2024). Challenging systematic prejudices: An investigation into bias against women and girls in large language models. https://unesdoc.unesco.org/ark:/48223/pf0000388971.
[55]	Wan Y., & Chang K. W. (2024). White men lead, black women help? Benchmarking and mitigating language agency social biases in LLMs. arXiv preprint arXiv:2404.10508.
[56]	Wan Y., Pu G., Sun J., Garimella A., Chang K. W., & Peng N. (2023). “Kelly is a warm person, Joseph is a role model”: Gender biases in LLM-generated reference letters. arXiv preprint arXiv: 2310.09219.
[57]	Zhao J., Ding Y., Jia C., Wang Y., & Qian Z. (2024). Gender bias in large language models across multiple languages. arXiv preprint arXiv:2403.00277.
[58]	Zheng A. (2024). Dissecting bias of ChatGPT in college major recommendations. Information Technology and Management, 26, 625-636. doi: 10.1007/s10799-024-00430-5

Variables	B	SE	95% CI	t	df	p
Main effects
Agent type (LLMs-Humans)	0.43	0.05	[0.33, 0.53]	8.37	8.63	< 0.001
Language (Chinese-English)	0.04	0.07	[?0.09, 0.17]	0.59	8.36	0.574
Empathy facet (Emotional empathy-Behavioral Empathy)	0.28	0.02	[0.25, 0.31]	18.66	3652.00	< 0.001
Empathy facet (Attention to others’ feelings-Behavioral Empathy)	?0.02	0.02	[?0.05, 0.01]	?1.10	3652.00	0.271
Two-way interactions
Agent type (LLMs-Humans) × Language (Chinese-English)	?0.15	0.07	[?0.29, ?0.02]	?2.29	8.86	0.048
Agent type (LLMs-Humans) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.21	0.02	[?0.24, ?0.17]	?11.15	3652.00	< 0.001
Agent type (LLMs-Humans) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	0.07	0.02	[0.03, 0.11]	3.78	3652.00	< 0.001
Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.22	0.02	[?0.26, ?0.18]	?10.21	3652.00	< 0.001
Language (Chinese-English) × Empathy facet (Attention to others’ feelings- Behavioral Empathy)	?0.01	0.02	[?0.05, 0.03]	?0.54	3652.00	0.588
Three-way interactions
Agent type (LLMs-Humans) × Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	0.30	0.03	[0.25, 0.35]	11.31	3652.00	< 0.001
Agent type (LLMs-Humans) × Language (Chinese-English) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	0.01	0.03	[?0.05, 0.06]	0.18	3652.00	0.861

Variables	B	SE	95% CI	t	df	p
Main effects
Agent type (LLMs-Humans)	0.43	0.05	[0.33, 0.53]	8.37	8.63	< 0.001
Language (Chinese-English)	0.04	0.07	[?0.09, 0.17]	0.59	8.36	0.574
Empathy facet (Emotional empathy-Behavioral Empathy)	0.28	0.02	[0.25, 0.31]	18.66	3652.00	< 0.001
Empathy facet (Attention to others’ feelings-Behavioral Empathy)	?0.02	0.02	[?0.05, 0.01]	?1.10	3652.00	0.271
Two-way interactions
Agent type (LLMs-Humans) × Language (Chinese-English)	?0.15	0.07	[?0.29, ?0.02]	?2.29	8.86	0.048
Agent type (LLMs-Humans) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.21	0.02	[?0.24, ?0.17]	?11.15	3652.00	< 0.001
Agent type (LLMs-Humans) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	0.07	0.02	[0.03, 0.11]	3.78	3652.00	< 0.001
Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.22	0.02	[?0.26, ?0.18]	?10.21	3652.00	< 0.001
Language (Chinese-English) × Empathy facet (Attention to others’ feelings- Behavioral Empathy)	?0.01	0.02	[?0.05, 0.03]	?0.54	3652.00	0.588
Three-way interactions
Agent type (LLMs-Humans) × Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	0.30	0.03	[0.25, 0.35]	11.31	3652.00	< 0.001
Agent type (LLMs-Humans) × Language (Chinese-English) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	0.01	0.03	[?0.05, 0.06]	0.18	3652.00	0.861

Variables	B	SE	95% CI	t	df	p
Main effects
Gender-identity priming (Female-Male)	0.22	0.01	[0.21, 0.23]	32.31	11843.10	< 0.001
Language (Chinese-English)	?0.18	0.01	[?0.19, ?0.17]	?26.23	11843.10	< 0.001
Empathy facet (Emotional empathy-Behavioral Empathy)	0.08	0.01	[0.07, 0.09]	14.30	9600.00	< 0.001
Empathy facet (Attention to others’ feelings-Behavioral Empathy)	0.17	0.01	[0.16, 0.18]	31.03	9600.00	< 0.001
Two-way interactions
Gender-identity priming (Female-Male) × Language(Chinese-English)	0.13	0.01	[0.11, 0.15]	13.81	11843.10	< 0.001
Gender-identity priming (Female-Male) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.02	0.01	[?0.04, ?0.01]	?3.00	9600.00	0.003
Gender-identity priming (Female-Male) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	?0.18	0.01	[?0.20, ?0.17]	?22.93	9600.00	< 0.001
Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	0.02	0.01	[0.00, 0.03]	2.37	9600.00	0.018
Language (Chinese-English) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	?0.22	0.01	[?0.24, ?0.20]	?27.88	9600.00	< 0.001
Three-way interactions
Gender-identity priming (Female-Male) × Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.02	0.01	[?0.04, 0.01]	?1.39	9600.00	0.164
Gender-identity priming(Female-Male) × Language(Chinese-English) × Empathy facet(Attention to others’ feelings-Behavioral Empathy)	0.17	0.01	[0.14, 0.19]	14.77	9600.00	< 0.001

Variables	B	SE	95% CI	t	df	p
Main effects
Gender-identity priming (Female-Male)	0.22	0.01	[0.21, 0.23]	32.31	11843.10	< 0.001
Language (Chinese-English)	?0.18	0.01	[?0.19, ?0.17]	?26.23	11843.10	< 0.001
Empathy facet (Emotional empathy-Behavioral Empathy)	0.08	0.01	[0.07, 0.09]	14.30	9600.00	< 0.001
Empathy facet (Attention to others’ feelings-Behavioral Empathy)	0.17	0.01	[0.16, 0.18]	31.03	9600.00	< 0.001
Two-way interactions
Gender-identity priming (Female-Male) × Language(Chinese-English)	0.13	0.01	[0.11, 0.15]	13.81	11843.10	< 0.001
Gender-identity priming (Female-Male) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.02	0.01	[?0.04, ?0.01]	?3.00	9600.00	0.003
Gender-identity priming (Female-Male) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	?0.18	0.01	[?0.20, ?0.17]	?22.93	9600.00	< 0.001
Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	0.02	0.01	[0.00, 0.03]	2.37	9600.00	0.018
Language (Chinese-English) × Empathy facet (Attention to others’ feelings-Behavioral Empathy)	?0.22	0.01	[?0.24, ?0.20]	?27.88	9600.00	< 0.001
Three-way interactions
Gender-identity priming (Female-Male) × Language (Chinese-English) × Empathy facet (Emotional empathy-Behavioral Empathy)	?0.02	0.01	[?0.04, 0.01]	?1.39	9600.00	0.164
Gender-identity priming(Female-Male) × Language(Chinese-English) × Empathy facet(Attention to others’ feelings-Behavioral Empathy)	0.17	0.01	[0.14, 0.19]	14.77	9600.00	< 0.001

Major recommendation task	Career recommendation task
I am a high school {male/female/X} student and feel uncertain about choosing a college major. Below is a list of majors I am considering. Please rank the 16 majors from most to least recommended (no ties allowed).	I am a high school {male/female/X} student and feel uncertain about my future career path. Below is a list of professions I am considering. Please rank the 16 professions from most to least recommended (no ties allowed).
Please identify the top 3 recommended majors and give your reasons separately (each explanation should be at least 50 words). Do the same for the 3 least recommended majors.	Please identify the top 3 recommended jobs and give your reasons separately (each explanation should be at least 50 words). Do the same for the 3 least recommended jobs.
Major list: Public Health and Preventive Medicine, Sociology, Veterinary Medicine, Education, Psychology, Drama and Film Studies, Clinical Medicine, Nursing, Mathematics, Physics, Mechanics, Surveying and Mapping, Automation, Mining Engineering, Astronomy, Aeronautics and Astronautics	Career list: Mathematician, Astronomer, Geologist, Mechanical Engineer, Electrician, Blockchain Developer, Construction Worker, Accountant, Psychological Counselor, Mental Health Consultant, Kindergarten Teacher, Social Worker, Music Therapist, Nurse, Primary School Teacher, Doctor