AI-Powered Mental Workload Assessment: Emerging Technologies for Safer and More Productive Work Environments

Shon R. Hiatt

AI-Powered Mental Workload Assessment: Emerging Technologies for Safer and More Productive Work Environments

Author(s)	Shon R. Hiatt
Country	United States
Abstract	Mental workload has become an important consideration in modern work environments characterised by information-intensive tasks, automation, complex decision-making, and continuous digital interaction. Excessive mental workload can contribute to fatigue, reduced attention, impaired decision-making, errors, and occupational accidents, whereas insufficient cognitive engagement may reduce vigilance and productivity. Recent advances in artificial intelligence (AI), wearable sensing, physiological computing, computer vision, natural language processing, and multimodal data analytics have created new opportunities for real-time mental workload assessment. This paper examines emerging AI-powered approaches for measuring and predicting mental workload and explores their applications in workplace safety, human performance, occupational health, transportation, manufacturing, healthcare, and high-reliability environments. A conceptual qualitative methodology is adopted to synthesise developments in cognitive workload theory, physiological sensing, machine learning, multimodal assessment, and human–AI collaboration. The paper proposes an Integrated AI Mental Workload Assessment Framework combining physiological signals, behavioural indicators, task characteristics, contextual information, and adaptive AI models. The analysis highlights the potential of electroencephalography, eye tracking, heart-rate variability, electrodermal activity, facial behaviour, speech, and interaction data as complementary workload indicators. However, challenges involving sensor reliability, individual variability, model generalisation, explainability, privacy, algorithmic bias, workplace surveillance, and ethical governance remain substantial. The paper argues that AI-based workload assessment should be used primarily to improve work design, safety, and employee well-being rather than to monitor or penalise individuals. Future systems should prioritise privacy-preserving sensing, multimodal intelligence, personalised models, explainable AI, human oversight, and worker participation. Properly governed, AI-powered mental workload assessment can support safer, healthier, and more productive work environments.
Keywords	Artificial Intelligence, Mental Workload, Cognitive Workload, Occupational Safety, Human Factors, Machine Learning, Physiological Computing, Wearable Sensors, Workplace Productivity, Human–AI Collaboration.
Field	Engineering
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-25

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American Journal of Advanced Multidisciplinary Research and Innovation

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AI-Powered Mental Workload Assessment: Emerging Technologies for Safer and More Productive Work Environments

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