AI-Augmented Detection and Mitigation of Developer Burnout in Remote Software Teams

Remote software teams face heightened risks of developer burnout due to communication overload, meeting fatigue, and reduced interpersonal cues. Traditional burnout detection relies heavily on self-reports and post-hoc surveys, which often miss early warning signs. This paper proposes a lightweight, AI-driven framework to identify early burnout signals using natural language processing (NLP), sentiment analysis, and short-interval pulse checks. By integrating insights from Slack, Zoom, and GitHub activity with mood trend aggregation, our system aims to surface actionable team-level interventions before chronic fatigue sets in. We analyze existing literature, identify implementation challenges, and suggest design recommendations for responsible deployment in engineering organizations.