A.C.M. FONG
ICT Cluster, Singapore Institute of Technology (SIT)
Department of Computer Science, Western Michigan University
alvis.fong@singaporetech.edu.sg
Fong, A. C. M. (2024). Engaging external partners in research: A longitudinal case study involving academic and non-academic entities [Poster presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-acmfong/
SUB-THEME
Opportunities from Engaging Communities
KEYWORDS
Industry engagement, government engagement, multipartite research, cybertraining research, AI readiness, workforce development
CATEGORY
Poster Presentation
INTRODUCTION
Members of the Western Michigan Transformative Interdisciplinary Human+AI Research group, together with external partners, have been engaged in multiyear research aimed at rapidly getting a broad spectrum of STEM learners AI ready. The on-going research has been funded by two consecutive CyberTraining grants from the U.S. National Science Foundation (NSF). As principal investigator of the grants, the author wishes to share experiences and explore potentially transferable knowledge in engaging with academic and non-academic partners. These include faculty members at U.S. and international academic institutions, scientists and engineers in tech companies (e.g., Amazon, Google, and Meta), and other experts in relevant government agencies. These stakeholders collectively steer research directions, shape current debate on safe, secure, and reliable AI, and contribute towards a sustainable ecosystem for advances in AI technologies and workforce development.
STUDY BACKGROUND
The CyberTraining program emphasizes research in both workforce/curricular development and community building (NSF CyberTraining program 2024). The author’s research team was first awarded a CyberTraining grant in 2020 to conduct a pilot study titled “Modular Experiential Learning for Safe, Secure, and Reliable AI” from 2020 to 2022 (NSF CyberTraining pilot grant, 2020). The team was subsequently awarded another a 4-year implementation grant titled “Promoting AI Readiness for Machine-Assisted Secure Data Analysis” in 2023 (NSF CyberTraining implementation grant, 2023). President Biden’s subsequent executive order regarding safe, secure, and trustworthy AI in 2023 further underscores the importance of this area of research (The White House, 2023). The study involves training students to development AI–ready knowledge and skills in both undergraduate and graduate populations. This presentation focuses on the community engagement aspect. In particular, it examines how such engagement can enrich students’ learning experiences.
COMMUNITY ENGAGEMENT
Figure 1 summarizes the key stakeholders in the on-going research. The core research team is supported by partners that come from a broad range of academic and non-academic organizations. Together, all these stakeholders aim to achieve collective impact with a shared agenda according to Kania J. and Kramer (2011).
Figure 1. Key project stakeholders for community building
Community engagement with academic partners
Academic partners that have supported include faculty from other U.S. 4-year universities and 2-year colleges, and universities in Singapore, Canada, and New Zealand. In addition to computer science, other quantitative disciplines represented include branches of engineering (civil, mechanical and aerospace, electrical, etc.), statistics, business analytics, etc. They are current and future users of AI. In addition to providing guidance on research directions, these multidisciplinary experts add relevance to applied AI with realistic examples of AI use cases drawn from their disciplines. Examples: mechanical engineers using AI to optimize vehicle drive cycles for fuel efficiency, civil engineers using AI for smart traffic management, statisticians using AI to visualize complex data, etc. Many have also field tested the new learning materials in their respective settings and helped collected anonymized use data.
Community engagement with non-academic partners
Though perpetually busy in their lines of work, industry and government experts from several organizations have provided valuable advice to the research team. Their guidance ensures that all curricular development activities and artifacts are relevant, up-to-date, and geared towards achieving optimal learning outcomes. Table 1 summarizes the main non-academic partners.
Table 1
Main non-academic research partners
Further outreach for broader impacts
Since summer 2022, the team has been involved in outreach activities to broaden the research impacts. The general formula entails a) customization of some developed learning materials to make them accessible and b) field testing the customized materials in local area high schools. Informal feedback from affected high schools has been positive.
CONCLUSION
This presentation has highlighted multiparty community engagement by the author over several years of funded cybertraining research. The ongoing research has a strong emphasis on community building. This presentation aims to share experiences in community engagement across a broad spectrum of disciplines and organizations. Opportunities for cross-fertilization likely follow.
REFERENCES
Kania J., & Kramer M. (2024). Collective Impact, Stanford Social Innovation Review.
NSF CyberTraining research program (2024). Available at https://new.nsf.gov/funding/opportunities/training-based-workforce-development-advanced
NSF CyberTraining pilot grant number 2017289 (2020). Available at https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017289&HistoricalAwards=false
NSF CyberTraining implementation grant number 2320951 (n.d.). Available at https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320951&HistoricalAwards=false
The White House (2023). President Biden’s Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence (Oct 2023). Available at https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/