CAREER: Problem Partitioning and Division of Labor for Human-Computer Collaboration in Engineering Design

The objective of this Faculty Early Career Development (CAREER) research project is to understand how the allocation of collective design tasks into teams of humans and Artificial Intelligence (AI)-enabled computer agents with diverse decision-making characteristics will affect design outcomes. The overarching premise of this research project is that the current organization of design teams around analysis disciplines (e.g., thermal analysis, structural analysis) or around physical components (e.g., engine, battery, exterior body) may not be the ideal way to architect human-AI teams.

Considering the differences between humans and AI systems in terms of their capabilities, there may be alternative ways to divide tasks and responsibilities in system-level design problems among humans and artificial members within a team. This research will contribute to the field of design science through a systematic and comprehensive analysis of team architectures in hybrid human-AI teams and the impact of those different architectures on design outcomes.

Rather than assuming a pre-defined role for AI, this project will follow a top-down Systems Engineering approach to identify best practices for defining roles for AI in a design team using computational models of human and AI decision-making processes. An experimental study using a video game platform will engage human users to work alongside AI teammates in solving a design problem to assess the impact of human factors in this context as well.

The findings will inform how AI technology should be integrated into the engineering design workforce with proper task allocation in order to reduce system development time and costs for future enterprises in multiple industries, spanning from smart healthcare to defense. The project will generate broader impacts by engaging a diverse group of undergraduate students into research using the STEM-SI program at Lehigh University.

Integrated with the research program, the education plan will improve pedagogical practices in statistics and machine learning for engineering systems applications using e-training games. Outreach workshops within local communities will attract broader interest in data science by engaging middle school girls in data-related challenges in engineering using the CHOICES program at Lehigh University.

This research addresses the lack of fundamental principles to guide task partitioning and division of labor for hybrid human-AI teams, accounting not only for heterogeneity among decision-makers (represented by a select set of characteristics), but also for important human factors. The project will use multi-agent simulations to model generalized agents that solve context-free design problems following Bayesian decision-making processes.

These agents will be characterized in terms of task performance, self-confidence, and confidence in other team members. A computational analysis will use various problem partitioning and task assignment strategies from decomposition-based design and machine learning to quantify their impact on team collaboration considering diverse agent characteristics.

Mirroring this simulation scenario, behavioral experiments on an electric vehicle design and control game will present a collaborative design decision-making problem for hybrid human-AI teams with alternative task allocation scenarios in a controlled setting. These experiments will collect behavioral data that capture the effects of human factors, including bias, workload, and job satisfaction, and that will be used to validate or refine the computational findings.

As a by-product, this research will develop an open infrastructure to study human-AI collaboration in design teams by sharing the experimental platform with the broader scientific community. This project will also integrate video game platforms developed for the behavioral study into existing mechanical engineering courses and data bootcamps to teach undergraduate and graduate students data analytics.

Outreach workshops will use the same game platforms to increase data literacy and promote STEM careers among middle school girls.

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.