EGI: A Multimodal Emotional AI Framework for Enhancing Scrum Master Real-time Self-Awareness
While increasing research focuses on the emotional well-being of agile team members, a significant gap remains in emotion monitoring studies for Scrum Masters and meeting organizers, whose impact on team dynamics is crucial. This paper proposes a novel application integrating four carefully selected and recommended AI models to monitor the unconsciously expressed emotions of these key roles. This is achieved through: real- time transcription using a speech-to-text model; thresholding for intonation analysis to detect emotional cues in prosody; applying emotion-based vocabulary matching to identify sentiment in spoken content; and providing context-aware suggestions containing emotion keyw...
| Authors | Jingni Huang, Peter Bloodsworth |
|---|---|
| Primary category | cs.AI |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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