Worker-AI Teaming in the Construction Industry of the Future
Project Description
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, 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. This project develops an AI-driven learning platform to facilitate the deployment of AI teammates in construction workplaces, thereby enhancing employment opportunities and safety outcomes for construction workers with varying abilities. To lay the necessary foundations for building this human-AI teaming workspace for construction workers, this proof-of-concept project will translate non-invasive biomechanical and neuro-psychophysiological responses into information that 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 privacy threats.
Start Date
January 15, 2026
Postdoc Qualifications
Educational background
- A recent Ph.D. or doctoral degree (within the last 5 years) in Human Factors, Industrial Engineering, Systems Engineering, Cognitive Psychology, Experimental Psychology, Computer Science, or a closely related field.
- Demonstrated experience in a specialized area, such as human factors and human-robot interaction.
Research experience and skills
- Research methods: Proficiency in both quantitative and qualitative research methodologies, including experimental design, usability testing, task analysis, and user research.
- Data analysis: Strong statistical and analytical skills with experience using software like MATLAB, Python, R, or SPSS.
- Technical skills: Strong programming or technical skills relevant to the position.
- Domain-specific knowledge related to Human-robot interaction: Gait biomechanics, wearable robotics, reinforcement learning.
- Publication record: A strong record of peer-reviewed publications is often required, demonstrating the ability to contribute substantively as a lead or co-author.
- Writing and presentation: Excellent oral and written communication skills to effectively communicate research findings, write grant proposals, and prepare technical reports.
- • Teamwork: The ability to work independently and collaboratively within a multidisciplinary team is essential. Many roles involve working with engineers, clinicians, and other domain experts.
Co-advisors
Sébastien Hélie
Department of Psychological Sciences
College of Health and Human Sciences
Email: shelie@purdue.edu
https://hhs.purdue.edu/directory/sebastien-helie/
Bibliography
- Chang, W. C., Esmaeili, B., & Hasanzadeh, S. (2025). Impacts of Physical and Informational Failures on Worker–Autonomy Trust in Future Construction. Journal of Construction Engineering and Management, 151(4), 04025011.
- Lee, K., Pooladvand, S., Esmaeili, B., & Hasanzadeh, S. (2024). Understanding construction workers’ risk perception using neurophysiological responses. Journal of Computing in Civil Engineering, 38(6), 04024039.
- Onuchukwu, I. S., Esmaeili, B., & Hélie, S. (2024). Application of automaticity theory in construction. Journal of Management in Engineering, 40(3), 04024018.
- Hasanzadeh, S., Esmaeili, B., & Dodd, M. D. (2017). Measuring the impacts of safety knowledge on construction workers’ attentional allocation and hazard detection using remote eye-tracking technology. Journal of management in engineering, 33(5), 04017024.
- Hasanzadeh, S., Esmaeili, B., & Dodd, M. D. (2017). Impact of construction workers’ hazard identification skills on their visual attention. Journal of construction engineering and management, 143(10), 04017070.