Date of Award
8-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Human Centered Computing
Committee Chair/Advisor
Nathan McNeese
Committee Member
Golnaz Arastoopour Irgen
Committee Member
Guo Freeman
Committee Member
Emma Dixon
Abstract
The integration of Artificial Intelligence (AI) in the workforce is transforming team dynamics, leading to the emergence of Human-AI Teams (HATs). These teams offer opportunities to capitalize on human strengths with AI's prowess, offering significant opportunities for innovation and efficiency. Effective HAT functioning requires aligning human expectations with AI capabilities and bridging knowledge gaps between teammates. Despite this potential, key integration challenges remain, such as developing shared mental models, addressing skill limitations, and overcoming negative AI perceptions. Existing training efforts often apply human-human teaming principles directly to HATs, overlooking AI's role as a teammate and limiting the development of HAT-specific training programs. This dissertation addresses this gap through three research projects focused on tailored training to enable HATs to thrive. Study 1 investigated macro-level training needs for HATs, focusing on individuals who interact and train with AI teammates in eSports. Using quantitative ethnography and epistemic network analysis of interviews, the study reveals an overemphasis on task-oriented skills in current training, highlighting a gap in supporting teamwork and understanding AI teammates. Participants identified cross-training as essential for building collaboration and understanding AI roles, emphasizing the need for training that enhances interpersonal dynamics, trust, and nuanced AI understanding. Study 2, an online mixed-methods experiment, assessed different team role assignments and cross-training types to support HAT-relevant knowledge, skills, and attitudes. Using a chatGPT-based AI teammate, the study finds positional modeling enhances understanding of HAT interdependencies, shared understanding, trust, AI acceptance, and overall team effectiveness. Study 3 evaluated HAT team training effectiveness in an organizational setting, testing strategies focused on AI situational awareness, communication, and human-AI interdependencies. Using mixed methods, the study showed that training can improve performance, trust, critical awareness, and willingness to adopt HATs. These studies fill a research gap in HAT training, proposing methods to set expectations and support AI understanding, easing HAT integration. The dissertation's contributions can guide future research and practical applications, which is essential as AI reshapes the workplace.
Recommended Citation
Lancaster, Caitlin M., "We Train AI, Why Not Humans, Too? An Exploration of Human-AI Team Training for Future Workplace Viability" (2024). All Dissertations. 3742.
https://open.clemson.edu/all_dissertations/3742
Author ORCID Identifier
0000-0002-1479-7597
Included in
Artificial Intelligence and Robotics Commons, Graphics and Human Computer Interfaces Commons, Science and Technology Studies Commons