ArticleScientific reports2024
Large language models can outperform humans in social situational judgments.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Population-dependent agent performance in non-transitive games: a multi-agent rock-paper-scissors benchmark.Scientific reports · 2026Article
- A systematic review and meta-analysis of psychological and behavioural responses in human-agent vs. human-human interactions.Communications psychology · 2026Article
- The AI interviewer: multi-faceted evaluation of adaptive questioning by large language models.Scientific reports · 2026Article
- Exploring the potential of large language models in nutrition behavior prediction: evidence from college students.Frontiers in nutrition · 2026Article
- Reasoning-based LLMs surpass average human performance on medical social skills.Scientific reports · 2025Article
- Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Review
- An intelligent guided troubleshooting method for aircraft based on HybirdRAG.Scientific reports · 2025Article
- Article
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5 authors.
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Abstract
Large language models (LLM) have been a catalyst for the public interest in artificial intelligence (AI). These technologies perform some knowledge-based tasks better and faster than human beings. However, whether AIs can correctly assess social situations and devise socially appropriate behavior, is still unclear. We conducted an established Situational Judgment Test (SJT) with five different chatbots and compared their results with responses of human participants (N = 276). Claude, Copilot and you.com's smart assistant performed significantly better than humans in proposing suitable behaviors in social situations. Moreover, their effectiveness rating of different behavior options aligned well with expert ratings. These results indicate that LLMs are capable of producing adept social judgments. While this constitutes an important requirement for the use as virtual social assistants, challenges and risks are still associated with their wide-spread use in social contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.