In March, I shared the an overview of the “Practitioner to Professor (P2P)‘ survey that the CRA-Education / CRA-Industry working group analyzed. They recently released a report titled Breadth of Practices in Academia-Industry Relationships which explores a range of engagement models from research partnerships and personnel exchanges to master agreements and regional innovation ecosystems.
Key Findings and Observations
The report organizes its findings from the workshop into three categories: observations, barriers, and common solutions:
Observations A major theme was the critical need to embed ethical training into AI and computing curricula through both standalone courses and integrated assignments. It was noted that while academia is best suited to drive curriculum development, input from industry is essential to ensure the content remains relevant to real-world applications.
Barriers Key barriers to successful collaboration were identified, including cultural differences and misconceptions between academic and industry partners. For instance, industry’s focus on near-term goals can clash with academia’s long-term vision. A significant practical barrier is the prohibitive cost of cloud and GPU hardware, which limits students’ experience with cloud and AI development tools.
Common Solutions Effective solutions include the fluid movement of personnel between organizations through internships, co-ops, sabbaticals, and dual appointments. Streamlined master agreements at the institutional level also help facilitate research collaborations by reducing administrative friction.
Strategies for Research Collaboration
The report outlines a multi-level approach to enhancing research partnerships:
Individuals Faculty and industry researchers can initiate relationships through internal seed grants, sabbaticals in industry, dual appointments, and by serving on industry advisory boards.
Departments Departmental leaders can foster collaboration by strategically matching faculty expertise with industry needs, offering administrative support, and building a strong departmental brand with local industry.
University Leadership Senior leaders can address systemic barriers by creating a unified, institution-wide strategy, developing flexible funding models, and implementing master agreements to streamline partnerships.
Regional Ecosystems The report emphasizes the importance of universities partnering with local industries and startups to build thriving regional innovation ecosystems, which can drive economic development and secure government support.
Education and Workforce Development
With the rise of generative AI, the report highlights an urgent need for universities and industry to partner on education.
Curriculum Adaptation Computing curricula need to be updated to include foundational concepts in DevOps and scalable systems, which are often not part of the core curriculum. While AI literacy is essential, the report suggests a balance, with 80% of instruction remaining focused on core computer science skills. Ethical reasoning should be integrated throughout the curriculum, not just in a single course.
Workforce Programs To meet industry demands for job-ready graduates, the report advocates for university-industry partnerships in co-op programs, internships, and capstone projects. It also points to the need for universities to offer flexible programs like certificates and online courses to help upskill and reskill the existing workforce.
Recommendations
The report concludes with five main recommendations for universities, industry, and government:
Enhance research impact by combining academia’s long-term vision with real-world problems from industry. This can be achieved by embedding faculty in industry and industry researchers in universities.
Leverage the convening power of universities to build partnerships that benefit the wider community, using mechanisms like industrial advisory boards and research institutes.
Accelerate workforce development by aligning university programs with regional innovation ecosystems and having industry invest in talent through fellowships and internships.
Deliver industry-relevant curricula grounded in core computing principles, and collaborate with industry experts to co-design courses in high-demand areas like AI and cloud computing.
Establish new incentives and metrics to recognize and reward faculty for their contributions to industry partnerships in promotion and tenure evaluations.
AI Teaching and Learning Podcasts:What If College Teaching Was Redesigned With AI In Mind?
https://learningcurve.fm/episodes/what-if-college-teaching-was-redesigned-with-ai-in-mind
A former university president is trying to reimagine college teaching with AI in mind, and this year he released an unusual video that provides a kind of artist’s sketch of what that could look like. For this episode, I talk through the video with that leader, Paul LeBlanc, and get some reaction to the model from longtime teaching expert Maha Bali, a professor of practice at the Center for Learning and Teaching at the American University in Cairo.
The Opposite of Cheating Podcast
https://open.spotify.com/show/5fhrnwUIWgFqZYBJWGIYml
(Produced by the authors of the book with the same name) the podcast shares the real life experiences, thoughts, and talents of educators and professionals who are working to teach for integrity in the age of AI. The series features engaging conversations with brilliant innovators, teachers, leaders, and practitioners who are both resisting and integrating GenAI into their lives. The central value undergirding everything is, of course, integrity!
Teaching in Higher Ed podcast, “Cultivating Critical AI Literacies with Maha Bali”.
https://teachinginhighered.com/podcast/cultivating-critical-ai-literacies/
In the episode, host Bonni Stachowiak and guest Maha Bali, a Professor of Practice at the American University in Cairo, explore the complexities of integrating artificial intelligence into higher education.
Bali advocates for a critical pedagogical approach, rooted in the work of Paulo Freire, urging educators to actively experiment with AI to understand its limitations and biases. The discussion highlights significant issues of cultural and implicit bias within AI systems. Bali provides concrete examples, such as AI generating historically inaccurate information about Egyptian culture, misrepresenting cultural symbols, and defaulting to stereotypes when prompted for examples of terrorism.
The Actual Intelligence podcast
speakswith Dr. Robert Neibuhr from ASU regarding his recent article in Insider Higher Ed: “A.I and Higher Ed: An Impending Collapse.” Full Podcast: https://podcasts.apple.com/us/podcast/is-higher-ed-to-collapse-from-a-i/id1274615583?i=1000725770519
with Bill Gates having just said that A.I. will replace most teachers within ten years, it seems essential that professional educators attune to the growing presence of A.I. in education, particularly its negative gravitational forces.