EGI: A Multimodal Emotional AI Framework for Enhancing Scrum Master Real-time Self-Awareness
The paper presents EGI, a multimodal emotional AI framework designed to enhance the real-time self-awareness of Scrum Masters. It integrates multiple AI models to monitor and analyze emotions during agile meetings, aiming to improve team dynamics. The system has shown promising results in simulated environments, significantly enhancing emotion awareness and providing actionable feedback.
- ▪EGI integrates four AI models to monitor emotions of Scrum Masters and meeting organizers.
- ▪The system achieved a 10% word error rate in simulated meeting environments.
- ▪Real-time feedback from EGI improves emotion awareness and fosters positive team interactions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17684 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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| Summary source text | contentText |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.17684 (cs) [Submitted on 17 May 2026] Title:EGI: A Multimodal Emotional AI Framework for Enhancing Scrum Master Real-time Self-Awareness Authors:Jingni Huang, Peter Bloodsworth View a PDF of the paper titled EGI: A Multimodal Emotional AI Framework for Enhancing Scrum Master Real-time Self-Awareness, by Jingni Huang and 1 other authors View PDF Abstract: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.
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