NSF Award
2420165
The recent dynamic evolution of mobility, marked by the integration of self-driving and human-operated vehicles, demands a paradigm shift toward predictive planning driven by advancements in Generative Artificial Intelligence (GenAI). Given the complexity of mixed mobility systems, a convergence approach is necessary. This approach could revolutionize our understanding of adaptive transportation and technology while fostering a diverse, globally engaged workforce with top-notch skills. To address this timely imperative, the project team uses an innovative, three-year US-Korea mutual workforce development initiative, hosting over 13 graduate students from each country annually for 6.5 weeks. This initiative aims to catalyze advanced international research alliances in next-generation workforce development by focusing on the seamless convergence of GenAI and disruptive technologies into unified student learning outcomes. Built upon the success of a prior IRES initiative, which has inspired a parallel Reverse US-Korea IRES initiative by the National Research Foundation of Korea, the proposed program focuses on groundbreaking research that integrates large language models into adaptable and responsive mobility solutions. This IRES program propels the development of a diverse, globally engaged workforce with world-class skills, as it is designed to function with enhanced intensity and efficiency towards fostering the exchange of novel ideas among IRES fellows. Such a collaborative environment within the overarching theme of ‘Mobility Solutions of the Future with GenAI’ will enable IRES fellows to address impending grand challenges in diverse contexts in order for them to develop globally applicable, verifiable, and repeatable theories, use cases, and algorithms. Six foreign collaborators at KAIST in Daejeon, South Korea, will contribute to transformative action learning opportunities and mentorship for the IRES fellows during their stay in South Korea. <br/><br/>The long-term goal of this project is to expedite the development of new knowledge and technologies in GenAI by cultivating a globally competitive and diverse research workforce. The main objectives of this IRES are twofold: first, to foster the growth of a skilled workforce in the emerging field of mobility-GenAI partnership, and second, to generate a new and effective mobility-GenAI knowledge use case database on an annual basis, which can then be integrated into a cohesive cyberlearning platform for sustainable workforce development. The research themes and their convergence in the project's final year will uniquely facilitate comparative analysis and predictive modeling of various management and operational scenarios. This acceleration can maximize societal benefits by fostering a highly safe, efficient, and sustainable future transportation system. Beyond transportation engineering, management, and operation, the interdisciplinary nature of this two-way US-Korea IRES research offers broader social benefits. It delves into understanding human-GenAI interactions concerning transportation policies and their consequential impacts on mobility, safety, and the environment. Collaboration with communities and industry practitioners equips IRES fellows with world-class skills to tackle the complexities and challenges of real-world problems. The proposed IRES workforce training activities are grounded in Experiential Learning Theory (ELT), which underscores the principle of ‘learning by doing.’ Following the ELT principles of knowledge creation and action learning, this proposal outlines a novel three-pronged pedagogical strategy: (1) the development of conceptualized use cases, (2) transitioning from traditional pedagogy to an entirely research-driven project-based action learning format, and (3) engaging use-infused research projects. This US-Korea mutual IRES is tailored to provide a robust foundation for theoretical and practical exploration, yielding groundbreaking insights into the interconnected impacts of mobility, safety, society, and global warming.<br/><br/>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.
