AgileSense-AI: An Emotion Perception and Intelligent Decision Support Platform for Agile Teams

AgileSense-AI is a research-driven AI platform that provides comprehensive intelligent support for agile teams. It integrates core functions like emotion perception analysis, expert recommendation, sprint impact prediction, and inclusive communication support, using natural language processing, multimodal machine learning, and decision support models to enhance team collaboration efficiency and project success rates.

Agile development has become a mainstream practice in software engineering, but teams face challenges such as delayed emotion perception, lack of scientific basis for task allocation, deviations between sprint planning and execution, and remote communication barriers. AgileSense-AI addresses these pain points with an AI-based integrated solution to enhance collaboration effectiveness through data-driven approaches.

The platform's core functions include:

1. Emotion Perception Analysis: Identify team members' emotions from multi-source data and alert to negative emotions;
2. Expert Recommendation System: Recommend suitable personnel for tasks based on skills, contributions, workload, etc.;
3. Sprint Impact Prediction Engine: Analyze historical and current data to assess the probability of sprint goal achievement and identify risks;
4. Inclusive Communication Support: Detect communication biases or exclusionary language and provide improvement suggestions.

The technical architecture uses multimodal fusion:

• Natural Language Processing Layer: Use BERT/RoBERTa to process text emotion and semantics, supporting multiple languages;
• Multimodal Machine Learning Layer: Integrate text, voice, and facial expression data to build emotion models;
• Decision Support Model Layer: Use time series analysis, causal inference, and reinforcement learning to build interpretable models;
• Data Pipeline and Privacy Protection: End-to-end process to ensure data anonymization and user authorization control.

Application scenarios include:

• Daily standup optimization: Automatically summarize status and identify issues that need in-depth discussion;
• Retrospective meeting enhancement: Provide data-supported improvement suggestions and quantify team evolution;
• Remote team management: Compensate for information gaps in distributed collaboration and maintain cohesion;
• New employee integration: Monitor adaptation status and provide personalized support;
• Project health assessment: Build a multi-dimensional health indicator dashboard.

The project adopts a research-driven methodology. Model algorithms are based on academic achievements in agile management and organizational psychology, referencing theoretical frameworks such as team dynamics and psychological safety. At the same time, it closely interacts with the agile community to absorb practical feedback for iterative improvement.

Future directions include: Deepening integration with tools like Jira and Azure DevOps; Developing more refined collaboration pattern recognition; Exploring the application of generative AI in document automation and knowledge management; Building a cross-organizational agile practice knowledge base.