Building a Framework for Academia-Industry Partnerships and AI Teaching and Learning Podcasts

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:

1. 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.

2. Leverage the convening power of universities to build partnerships that benefit the wider community, using mechanisms like industrial advisory boards and research institutes.

3. Accelerate workforce development by aligning university programs with regional innovation ecosystems and having industry invest in talent through fellowships and internships.

4. 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.

5. 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.

Bridging the Gap: What Tech Practitioners Really Want from Computer Science Education

In the spring of 2024, the Computing Research Association (CRA) asked a simple but powerful question: What do industry professionals think about the way we teach computer science today?  as part of a “Practitioner to Professor (P2P)‘ survey that the CRA-Education / CRA-Industry working group is doing.

The response was overwhelming. More than 1,000 experienced computing practitioners—most with over two decades of experience—shared their honest thoughts on how well today’s CS graduates are being prepared for the real world.

These weren’t just any professionals. Over three-quarters work in software development. Many manage technical teams. Most hold degrees in computer science, with Bachelor’s and Master’s being the most common. Half work for large companies, and a majority are employed by organizations at the heart of computing innovation.

So, what did they say?

The Call for More—and Better—Coursework

One of the loudest messages was clear: students need more coursework in core computer science subjects. Respondents recommended about four additional CS courses beyond what’s typical today. Algorithms, computer architecture, and theoretical foundations topped the list.

But it wasn’t just CS classes that practitioners wanted more of. They also suggested expanding foundational courses—especially in math, writing, and systems thinking. It turns out that the ability to write clearly, think statistically, and understand how complex systems interact is as critical as knowing how to code.

It’s Not Just About Programming

When it came to programming languages, the responses painted a nuanced picture. Practitioners agreed: learning to code isn’t the end goal—learning to think like a problem-solver is.

They valued depth over breadth. Knowing one language well was seen as more important than dabbling in many. But they also stressed the importance of being adaptable—able to pick up new languages independently and comfortable working with different paradigms.

Familiarity with object-oriented programming? Definitely a plus. But what mattered most was a student’s ability to approach problems critically, apply logic, and build solutions—regardless of the language.

The Soft Skills Shortfall

One of the most striking critiques was aimed not at technical training, but at the lack of soft skills being taught in undergraduate programs.

Soft skills, they argued, can be taught—but many universities simply aren’t doing it well. Oral communication courses were highlighted as a critical need. And interestingly, several respondents felt that liberal arts programs were doing a better job than engineering-focused ones in nurturing communication, collaboration, and leadership.

Asked to identify the most important communication skills, respondents pointed to the ability to speak confidently in small technical groups, write solid technical documentation, and explain ideas clearly to leaders and clients—both technical and non-technical.

Math Is Still a Must

Despite the rise of high-level frameworks and automation, the industry’s love affair with math is far from over. In fact, 65% of respondents said they enjoyed or pursued more math than their degree required.

Why? Because math is the backbone of emerging fields like AI, machine learning, and data science. It sharpens analytical thinking, cultivates discipline, and builds a foundation for lifelong adaptability.

The most important math subjects? Statistics topped the list, followed by linear algebra, discrete math, calculus, and logic.

Foundations First

The survey didn’t just surface high-level trends—it got specific.

In algorithms, the emphasis was on conceptual thinking, not just implementation. Students should deeply understand how algorithms work, why they matter, and how to analyze them.

In computer architecture, digital logic and memory hierarchy were considered essential. These are the building blocks that enable students to understand modern computing systems, from the ground up.

And when it came to databases? Practitioners wanted a balance: students should learn both the theory (like relational algebra and normalization) and the practice (like SQL and indexing). Real-world readiness depends on both.

Toward a Better Future for CS Education

What makes this survey so impactful is its timing and intent. As technology continues to reshape every industry, there’s a growing urgency to close the gap between academia and the workforce. The P2P Survey is part of a broader movement to do just that.

Endorsed by leading organizations—ABET, ACM, CSAB, and IEEE-CS—this initiative creates a powerful feedback loop between universities and the industry they serve.

So, what’s next? A full report is expected later this year. But the message is already loud and clear: today’s students need a curriculum that not only teaches them how to code, but prepares them to lead, adapt, and thrive in a complex, evolving world.