Vol 13 No 2 (December 2026): OPEN

Title: Deep Learning Framework for Burnout Prediction in IT Professionals with AI-Generated Personalized Music Therapy
Author(s): Pooja Sithrubi Gnanasambanthan, K. S. Gayathri, M. Gnana Priya
Abstract: Burnout has emerged as a serious occupational health concern among information technology (IT) professionals employed in multinational companies (MNCs), driven by unbalanced work schedules, extended working hours, high cognitive workload, and persistent digital connectivity. Such conditions contribute to chronic stress, sleep deprivation, reduced productivity, and adverse mental health outcomes. Conventional burnout assessment approaches are largely reactive, depending on self-reported measures and delayed interventions, limiting their effectiveness in preventive mental health care. To address this gap, this paper proposes a deep learning–based framework for early burnout prediction integrated with AI-generated personalized music therapy as an immediate intervention mechanism. The proposed system leverages multi-modal data inputs, including workload patterns, communication frequency, and self-reported mood indicators, to train a deep learning model capable of classifying burnout risk into low, medium, and high levels, drawing on established advances in deep learning and affective computing. For stress mitigation, a generative AI module synthesizes personalized instrumental music tailored to the user’s predicted burnout state, motivated by prior evidence on the efficacy of music in stress reduction and mood regulation [9], [10]. A prototype implementation is developed as a secure web-based application featuring real-time burnout monitoring, interactive dashboards, and a personalized music playback interface. Preliminary evaluations conducted using synthetic workload datasets and initial user feedback demonstrate the feasibility of combining predictive analytics with AI-driven music-based interventions to enhance employee well-being. The proposed framework highlights the potential of an intelligent, scalable solution for proactive burnout management in the software industry, bridging preventive mental health analytics with personalized therapeutic support.
DOI: 10.36079/lamintang.ijai-01302.XXXX