Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes
For human-robot interaction researchers, this exploratory study provides initial insights into how humor style and content shape perceptions of robot-delivered humor, though findings are incremental and limited to a specific context.
This study investigated how humor style, joke content, and language preference affect perceptions of robot-delivered jokes in a classroom setting. Results showed that humor type (Aggressive and Affiliative) significantly influenced funniness, while joke content (person-related vs. political) primarily affected appropriateness, with person-related jokes preferred.
Humor plays a central role in human social relationships, and recent advances in computational humor create new opportunities for integrating humor into human-robot interaction (HRI). While large language models (LLMs) can generate diverse forms of humor, it remains unclear how humor style, joke content, and language preference shape perceptions of robot-delivered humor in group settings. In this exploratory study, we employed a mixed factorial design in which participants evaluated AI-generated jokes delivered by a robot in a university classroom. We examined the effects of humor type (Affiliative, Self-Enhancing, Aggressive, Self-Defeating) and joke content (person-related vs. political) on perceived funniness and appropriateness, as well as preferred language. Results show that humor type significantly influences funniness, with Aggressive and Affiliative humor rated higher, while joke content primarily affects appropriateness, with person-related jokes preferred over political ones. Language preference was shaped by both joke content and participants' self-reported fluency and humor practices.