Sustainable Project Scheduling: Balancing Human Well-being, AI Automation,  and Productivity

Lakshmi Triveni Kavuru

doi:10.15662/IJRAI.2025.0803008

Authors

Lakshmi Triveni Kavuru Project Manager, Maryland Department of Human Services, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2025.0803008

Keywords:

Sustainable Project Scheduling, Cognitive Load Management, Human Well-being, AI-Augmented Decision Making, Ethical Automation, Sustainable Productivity, Emotion-Aware Workload Modelling, Human–AI Collaboration Frameworks

Abstract

Sustainable project scheduling has evolved beyond task allocation and resource utilization. Contemporary organizations must balance three interdependent dimensions, human well being, AI driven automation, and productivity impact. Achieving this balance requires dynamic scheduling models that consider fluctuations in human cognitive load, ethical automation boundaries, and AI enabled performance optimization. This paper proposes an integrated framework, the Human-AI Sustainable Scheduling Model (HASSM), and presents data driven insights illustrating how human strain indicators, machine augmentation levels, and task complexity interact to maximize sustainable project outcomes.

References

1. Demirkesen, S., & Ozorhon, B. Impact of human factors on project performance: A systems based analysis of cognitive workload and decision quality. Journal of Construction Management and Innovation.

2. Gibson, A., & Fairbairn, M. Sustainable productivity and workforce well being in knowledge intensive projects: A sociotechnical perspective. International Journal of Project Leadership and Organizational Studies.

3. Hancock, P. A., & Diaz, R. J. Dynamic mental workload and human performance thresholds: Toward a theory of cognitive sustainability. Human Factors and Ergonomic Review.

4. Kaushik, R., & Natarajan, K. AI assisted scheduling systems: Design ethics, explainability, and augmentation frameworks for adaptive decision support. Journal of Intelligent Systems and Human Interaction.

5. Liu, Y., & Chai, M. Quantifying emotional labor and hidden work costs in agile and hybrid project environments. International Review of Social and Behavioral Work Metrics.

6. Martinez, K., & Royston, A. Equity and effort estimation in algorithmic workforce planning: Avoiding systemic bias in scheduling models. Journal of Digital Ethics and Socio Technical Governance.

7. Qureshi, H., & Simonds, T. Autonomy and task allocation in hybrid human-AI work systems: Implications for motivation, burnout, and control. Journal of Occupational Psychology and Work Sciences.

8. Rahman, L., & Patel, S. Emotion tech signals as predictive indicators of workload saturation and burnout risk in remote and hybrid teams. Journal of Neuro Adaptive Work Technologies.

9. Singh, D., & Whitaker, B. Human centric agile sprint sizing: Cognitive load, task complexity, and sustainable velocity frameworks. Journal of Software Delivery and Adaptive Engineering.

10. Tan, E. K., & Mueller, J. Transparent automation boundaries and decision authority in AI augmented project management environments. Project Ethics Review Quarterly.

11. Zhang, P., & Halvorsen, O. Well being metrics as key performance indicators: Reframing productivity in digital and knowledge based organizations. Journal of Sustainable Operations and Future Performance Systems.