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Funded Research Projects

"Research: Exploring How AI Engineers Perceive and Develop Translational Ethical Competency"

August 2024 - Current

Funded by National Science Foundation (NSF) Award# 2412398

PI: Hoda Eldardiry (Department of Computer Science, VT)

Co-PIs: Dayoung Kim and Qin Zhu (Department of Engineering Education, VT)

Project Overview: The proliferation of Artificial Intelligence (AI)-enabled technologies is transforming the world. AI has brought promise and peril to nearly every aspect of human life. Nurturing AI engineers who can design AI systems responsibly has become a key objective from business and policy perspectives. For engineering educators committed to supporting students to become competent and responsible engineers, thoughtful consideration of how to effectively incorporate ethical principles into the classroom is paramount. However, translating existing research-based AI ethics tools, such as ethics principles, into engineering practice and education is not easy. Ethical principles are often too abstract to be integrated into design practices, as they do not provide clear explanations or procedural details on how they can be integrated into technical practice. Therefore, scholars have recently called for a transition in AI ethics research toward a translational approach, which focuses on ensuring that AI practitioners can implement AI ethics tools such as ethical principles into actual AI systems designs. However, there is a lack of empirical evidence for the extent to which engineers integrate these ethical principles into their daily practices of AI developers, which generates challenges for engineering educators to incorporate these resources into their own teaching and educational research. This research aims to advance the fundamental knowledge of the formation of future AI engineers poised to innovate responsibly and construct advanced engineering systems empowered by AI technologies. It will investigate a practical competency critical for socially responsible AI engineering. This includes translational ethical competency, or the ability to translate general ethical principles and values into specific decisions in AI engineering practices in the day-to-day work of AI engineers. 

"Building Capacity for Research in Technology-Based Social Entrepreneurship Education for the Next Generation of Engineering Leaders"

October 2023 - Current

Funded by National Science Foundation (NSF) Award# 2321188

PI: Dayoung Kim

Project Overview: Despite the emphasis on both entrepreneurship and social responsibility for engineering students, education and research on the two topics have been mostly separate efforts. Technology-based social entrepreneurship education for engineers has ample potential to give engineering students the opportunity to become socially responsible engineering leaders with an entrepreneurial mindset. However, very little research on the topic has been conducted in the field of engineering education. This project aims to advance understanding of how engineers who have launched technology social ventures identify business opportunities that can fulfill the goal of utilizing technology to solve societal problems and the required competencies for successful technology-based social entrepreneurship. Based on this knowledge, the project team will develop a measurement instrument to assess these competencies in undergraduate engineering students. The research findings and the resulting instrument will provide a basis for technology-based social entrepreneurship education for engineering students and students from other disciplines who intend to collaborate with engineers.

"Fostering Regional Innovation Ecosystem in the State of Virginia: Examining University Administrators’ Perspectives"

March 2023 - June 2023

Funded by 4-VA

PI: Dayoung Kim

Project Overview: Universities play a significant role in the economic and workforce development of the region. Particularly, regional universities with engineering programs can play instrumental roles in fostering the regional innovation ecosystem, a complex system that includes many components to facilitate entrepreneurial activities, by serving as a regional hub for technology entrepreneurship. One of the universities’ most commonly discussed roles in the regional innovation ecosystem is supplying students with entrepreneurial intentions. However, as regional contexts influence entrepreneurial activities in the region, the contexts may also affect how academic institutions contribute to the regional innovation ecosystem. This project aims to establish knowledge in the expert opinion on the roles, opportunities, and challenges of academic institutions in fostering the regional innovation ecosystem in the specific regional context, the state of Virginia.

 

"Exploring AI Ethics Policy Concerns and Career Pathways of the AI Professionals"

Oct 2022 - June 2023

Funded by the Institute for Society, Culture, and Environment - Policy Destination Area (ISCE-PDA), Virginia Tech

PI: Dayoung Kim

Co-PIs: Qin Zhu (Department of Engineering Education, VT) and Hoda Eldardiry (Department of Computer Science, VT)

Project Overview: Formulating effective ethics policies for AI is encountering at least two daunting challenges. First, policymakers are often behind the curve when it comes to designing ethics policies responsive to advances in AI. There has been a disconnect between ethics policies and guidelines and actual research and practice in AI. Effective AI ethics policymaking thus will benefit from the expertise and critical participation of AI professionals. It also requires that the professional curriculum for future AI scientists and engineers be informed by the expertise and concerns of working professionals. Second, in professional education, scientists and engineers have limited opportunities to learn about how to contribute to science and technology policymaking with their expertise. Students in AI-related professional programs are unaware of possible career pathways through which their expertise can contribute to policymaking or they can transition their future careers from AI-related fields to policy analysis or advocacy. To address these concerns, this project explores how scientists and engineers working in AI-related fields (e.g., machine learning, cybersecurity, robotics, and data analytics) particularly those who have received policy training as part of their professional education: (1) perceive ethics policy topics and skills central to their professional work; (2) have developed their careers that either focus on AI policy or are responsive to policy concerns.

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