ChatGPT outperforms humans in emotional awareness evaluations
Open accessAbstract
The artificial intelligence chatbot, ChatGPT, has gained widespread attention for its ability to perform natural language processing tasks and has the fastest-growing user base in history. Although ChatGPT has successfully generated theoretical information in multiple fields, its ability to identify and describe emotions is still unknown. Emotional awareness (EA), the ability to conceptualize one’s own and others’ emotions, is considered a transdiagnostic mechanism for psychopathology. This study utilized the Levels of Emotional Awareness Scale (LEAS) as an objective, performance-based test to analyze ChatGPT’s responses to twenty scenarios and compared its EA performance with that of the general population norms, as reported by a previous study. A second examination was performed one month later to measure EA improvement over time. Finally, two independent licensed psychologists evaluated the fit-to-context of ChatGPT’s EA responses. In the first examination, ChatGPT demonstrated significantly higher performance than the general population on all the LEAS scales (Z score = 2.84). In the second examination, ChatGPT’s performance significantly improved, almost reaching the maximum possible LEAS score (Z score = 4.26). Its accuracy levels were also extremely high (9.7/10). The study demonstrated that ChatGPT can generate appropriate EA responses, and that its performance may improve significantly over time. The study has theoretical and clinical implications, as ChatGPT can be used as part of cognitive training for clinical populations with EA impairments. In addition, ChatGPT’s EA-like abilities may facilitate psychiatric diagnosis and assessment and be used to enhance emotional language. Further research is warranted to better understand the potential benefits and risks of ChatGPT and refine it to promote mental health.
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ChatGPT demonstrates strong capabilities in emotional understanding.
“Nevertheless, ChatGPT demonstrates strong capabilities in emotional understanding, including generating appropriate affective responses and scoring highly on measures of emotional awareness [18, 69].”
From Emotional Coregulation in Close Relationships with AI Agents: A Survey of ChatGPT and Replika Users (2025) · cites this paper as [18]
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- Authors
- Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M.
- Affiliations
- The Max Stern Yezreel Valley College (IL)
- Corresponding author
- Elyoseph, Zohar
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2023-05-26
- Volume
- 14
- Pages
- 1199058-1199058
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC10254409
- OpenAlex
- W4378470708
- Cited by
- 324
- References
- 43
- Citations by year
- 2026: 63 · 2025: 122 · 2024: 117 · 2023: 22
- FWCI
- 83.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, emotional awareness, Levels of Emotional Awareness Scale, large language models, emotion recognition, AI chatbots, transdiagnostic mechanisms, psychopathology, cognitive training, psychiatric diagnosis, emotional expression, human-AI comparison
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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.
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