Emotional intelligence of Large Language Models
Open accessAbstract
Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI. This test is an objective, performance-driven, and text-based evaluation, which requires evaluating complex emotions in realistic scenarios, providing a consistent assessment for both human and LLM capabilities. With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average Emotional Quotient (EQ) scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not rely on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/
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Most LLMs achieve above-average scores in EQ, with GPT-4 scoring 117, surpassing 89% of human participants.
“The results showed that most LLMs achieve above-average scores in emotional quotient (EQ), with GPT-4 scoring 117, surpassing 89% of human participants.”
From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [212]
There are distinct differences in the quality of representational patterns of certain LLMs compared to humans.
“This is evident from the distinct differences in the quality of their representational patterns, as compared to humans.”
From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [212]
Certain LLMs achieve human-level performance without relying on mechanisms resembling those found in humans.
“However, a multivariate pattern analysis indicated that certain LLMs achieve human-level performance without relying on mechanisms resembling those found in humans.”
From A Survey on Evaluation of Large Language Models (2024) · cites this paper as [212]
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Liu, H., Lei, Y., & Wu, Z. (2025) · Science China Technological Sciences · doi:10.1007/s11431-025-2977-0 · Cited by 1 · see citing works
Behavioral psychology of LLMs: Better task guidance through punishment and reinforcement
Peng, S., Zhang, W., Li, S., Qu, D., & Wei, H. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131040 · Cited by 1 · see citing works
Performance of Large Language Models in the Cognitive Analysis of Misinformation: Evaluation Study
Wojtczak, D. N., McQuire, C., Zuccolo, L., Peersman, C., & McConville, R. (2025) · JMIR Infodemiology · doi:10.2196/72524 · Cited by 1 · see citing works
Xiao, Y., Yao, Z., Yang, S., Wang, X., & Lu, Y. (2024) · International Symposium of Chinese CHI (Chinese CHI) · doi:10.1145/3758871.3758905 · Cited by 1 · see citing works
Self-citationSimulating Feelings: LLM vs. Psychology-Based Models in Human-Robot Interaction
Corrao, F., Nardelli, A., Sgorbissa, A., & Recchiuto, C. T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2398-6_5 · Cited by 1 · see citing works
Almayez, M. (2026) · Language Testing in Asia · doi:10.1186/s40468-026-00432-w · Cited by 1 · see citing works
From Answer Engines to Learning Partners: A Dual-ZPD Design Framework for AI-Supported Learning
Klein, R., Benden, D. K., Schier, A., Stotko, D., & Lauermann, F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791476 · Cited by 3 · see citing works
Convergent Validation of Trait Induction in LLMs
Edin, Y., Rosenplänter, L., & Kersting, M. (2026) · Psychological Test Adaptation and Development · doi:10.1027/2698-1866/a000127 · Cited by 1 · see citing works
The Industrialization of Messaging Scams in the LLM Era
Gressel, G., Pankajakshan, R., Li, L., Franceschini, I., Achuthan, K., & Mirsky, Y. (2026) · doi:10.1145/3803629.3803676 · Cited by 1 · see citing works
Ibrahim, N., Sapiee, M. L., Sanuddin, N. D., Erhan, T., & Husain, F. C. (2024) · Environment and Social Psychology · doi:10.59429/esp.v9i8.2785 · Cited by 1 · see citing works
On the Cultural Sensitivity of Large Language Models: GPT's Ability to Simulate Human Self-Concept
Luo, Y., Du, J. S., & Qiu, L. (2024) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc64747.2024.10780488 · Cited by 1 · see citing works
Modifying Artificial Human Personas through Reality Therapy-based ArtiPsychotherapy
Kang, S., Lee, J.-Y., & Shin, K. (2024) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit62457.2024.10939965 · Cited by 1 · see citing works
Developing Self LLM to Enhance Student Knowledge Based on AIED for Personalized Learning Pathways
Fraoua, K. E., & DAVID, A. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93567-1_16 · Cited by 1 · see citing works
Xi, N., Wu, Y., Fan, K., Chen, T., Gu, Q., & Ji, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8186-0_3 · Cited by 1 · see citing works
Self-citationExploring Large Language Models’ Ability to Imitate Coping’s Influence on Beliefs and Goals
Yongsatianchot, N., & Marsella, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0695-5_31
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LLMs and Emotional Intelligence: Evaluating Emotional Understanding Through Psychometric Tools
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Boosting Self-efficacy and Performance of Large Language Models via Verbal Efficacy Stimulations
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Emotional Intelligence in Large Language Models: Fine-Tuning Methods, Challenges, and Applications
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CantastorIA: Enhancing Audiobook Engagement Through Adaptive Soundtracks and Voice Cloning
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Generative AI for Social Interaction Analysis: A Study of COVID ‐19 Video Comment Threads
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The Assessment of Body Image Based on Large Language Model
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Emotional Earth Mover’s Distance for Fine-Grained Hierarchical Emotion Analysis
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Do Autonomous Agents Exhibit Consistent Personality Traits in Open Social Environments?
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Multimodal LLMs for Emotion-Aware Human–Robot Interaction: Design and Implementation
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Beziehungsgestaltung im Zeitalter von KI-Technologien
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Retrieval Augmented Generation-based Large Language Model Chatbot
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Affect as Compensation: Self-Emotions Facilitate Low-Empathy Individuals’ Prosocial Behavior
Guo, R., Guo, Z., Su, Y., & Wu, Z. (2026) · Journal of Pacific Rim Psychology · doi:10.1177/18344909261416065
Measuring Social Media Polarization Using Large Language Models and Heuristic Rules
Chowdhury, J. M., Rashid, R., & Terejanu, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14107-1_35
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- Authors
- Wang, X., Li, X., Yin, Z., Wu, Y., & Liu, J.
- Affiliations
- Tsinghua University (CN)
- Renmin University of China (CN)
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
- Venue
- Journal of Pacific Rim Psychology
- Publisher
- SAGE Publications
- Published
- 2023-01
- Volume
- 17
- ISSN
- 1834-4909 (ISSN-L)
- OpenAlex
- W4388708748
- Cited by
- 128
- References
- 31
- Citations by year
- 2026: 41 · 2025: 59 · 2024: 27 · 2023: 1
- FWCI
- 33.02
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Pacific Rim Psychology (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Tsinghua University Guoqiang Institute (2020GQG1016)
- Shuimu Tsinghua Scholar Program
- Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park (Z221100002722012)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Emotional Intelligence and Performance
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- large language models, emotional intelligence, emotional understanding, emotional quotient, psychometric assessment, emotion recognition, GPT-4, model size, training methods, neural architecture, multivariate pattern analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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