FW-HTF-R: Collaborative Research: Worker-AI Teaming to Enable ADHD Workforce Participation in the Construction Industry of the Future

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While people with neurodiversity have been marginalized in the construction workplace due to potentially higher risks of injuries, their unique talents could be leveraged using an ecosystem of co-bots driven by artificial intelligence (AI). For humans and machines to become true teammates—and correlatively, for technology to extend occupational opportunities to people with such neurodiversity—intelligent machines must assess, adapt, and respond to both workers and their environment. Such agility requires a reciprocal teaming capability wherein workers can engage their AI counterparts as more than tools, and AI systems can collaborate with workers seamlessly by predicting their behaviors. To extend future occupational opportunities for people with neurodiversity, this project builds an AI-driven learning platform to enable distribution of AI teammates in construction workplaces to support employment opportunities and safety outcomes for construction workers with varying abilities. This study also investigates the intended work scenarios of worker-AI teaming, the unintended consequences of AI-teaming for workers, and the well-being of society. Considering that 4.2% of workers are diagnosed with attention-deficit/hyperactivity disorder (ADHD)—a disorder that is associated with more than 120 million lost workdays in the USA each year, equating to a human capital value of $19.5 billion—this project’s efforts to enable diverse workforce participation in the construction industry will have positive social and economic impacts. Additionally, this project will educate a new generation of leaders in worker-AI teaming and will create partnerships between academia and industry.To lay the necessary foundations for building this human-AI teaming workspace for construction workers with neurodiversity, this proof-of-concept project will translate non‐invasive biomechanical and neuro-psychophysiological responses into information a personalized AI-based training systems can assess, model, and leverage to predict workers’ behaviors for improved worker‐machine teaming without cultivating technological over-reliance or threats to privacy. In this project, a multidisciplinary team of researchers integrates expertise in civil engineering, computer science, cognitive and behavioral psychology, industrial engineering, and public policy and economics to address fundamental questions regarding the risk taking behavior and cognitive processes of workers with ADHD, barriers to adopting AI and wearable technologies, and the socioeconomic impacts of improved access to construction jobs for ADHD-diagnosed workers, especially in the context of interdependent human-AI partnerships. As this project’s global paradigm moves toward deeper human-machine teaming, the knowledge gained through this project advances the science and technology that influences diverse workforce development, education, and positive work outcomes for workers and society at large. By demonstrating the effectiveness of this AI-driven platform, this project illustrates how human-machine teams can progress on job sites and within communities across all sectors to augment human cognitive capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.