Another Crazy Day in AI: Teaching With and Through Technology

Wowza Team
Oct 29
5 min read

Hello, AI Enthusiasts.

Midweek check-in: how’s your pace?

In a recent podcast, Inside Higher Ed’s Ashley Mowreader speaks with Touro University’s Shlomo Argamon about what happens when universities lead with curiosity instead of fear when adopting AI. Their approach is simple: teach, test, and grow together.

Meanwhile, Google’s teaming up with Internet2 to help universities navigate that same journey responsibly through its AI Education Leadership Program.

And while educators explore how AI can teach, Adobe’s proving how AI can inspire. Their sweeping creative updates take it full circle, blending art, design, and intelligence into one seamless experience.

Still plenty of week left to make something new.

Here's another crazy day in AI:

The experimentation approach to AI in higher ed
Google teams up with Internet2 to advance AI in higher ed
Adobe showcases AI innovations across its creative suite
Some AI tools to try out

TODAY'S FEATURED ITEM: Faculty Experimentation and Student Practice

A robotic scientist in a classic white coat with 'AI Scientist' on its back stands beside a human scientist with 'Human Scientist' on their coat, looking towards the AI Scientist.

Image Credit: Wowza (created with Ideogram)

Can universities prepare students for an AI-driven workplace without losing what makes education fundamentally human?

In a recent episode of Voices of Student Success, Ashley Mowreader, reporter and podcast host at Inside Higher Ed, talks with Shlomo Argamon, Associate Provost for Artificial Intelligence at Touro University. Their conversation offers a grounded look at how one university is approaching the integration of AI into teaching and learning—not through sweeping mandates, but through curiosity, experimentation, and professional growth.

Touro University’s approach reflects a wider trend across higher education: finding responsible and meaningful ways to include AI in the classroom. Argamon describes how the institution is developing policies, faculty programs, and learning tools that help students and educators understand both the potential and the limits of these technologies.

What the discussion brings to light:

Ongoing faculty support – The university provides office hours, training sessions, and resources to help instructors navigate new teaching methods, acknowledging this creates additional work on top of existing responsibilities
AI woven into existing courses – General education includes discipline-specific modules where students learn to use these tools for writing, historical research, clinical practice, and other applications relevant to what they're already studying
Funded teaching experiments – Almost 100 faculty members received grants to test different methods of incorporating AI into instruction, with plans to share outcomes whether the experiments work as hoped or not
Flexibility within structure – The institutional policy establishes baseline expectations while giving individual schools, departments, and instructors authority to set their own classroom-specific guidelines
More practice opportunities – Students use AI-powered mannequins for medical training, receive automated feedback on presentations, and access simulations that let them practice skills outside regular class hours
Visibility into learning processes – Some tools show instructors the full development of student work, revealing which sections were drafted first, what got revised, and where students got stuck
Recognition of complications – The conversation addresses concerns about simulations becoming too game-like, maintaining genuine human interaction, and ensuring technology-mediated learning still develops empathy
Acceptance of uncertainty – The university treats AI integration as something that requires ongoing experimentation rather than presenting itself as having definitive answers

This conversation happens as many institutions grapple with similar questions. Recent data shows nearly 30 percent of college provosts have reviewed curriculum to address AI in professional settings, with another 63 percent planning to do so. But the distance between planning and doing is significant. How do you train faculty who already have full schedules? What does meaningful AI literacy look like in practice? When does a tool that's supposed to help students learn end up doing the learning for them?

Argamon doesn't pretend these questions have simple answers, which makes the conversation more useful than it would be otherwise. His description of Touro's work suggests an institution that's comfortable admitting it's figuring things out as it goes. The emphasis on letting faculty develop their own approaches recognizes that what works in a business course probably won't work the same way in a nursing program. The innovation grants acknowledge that some experiments won't pan out, and that's okay because the information gained from what doesn't work matters too. The university's baseline policy sounds clear—students can use AI to assist with work but not to complete it—until you start thinking about specific situations where that distinction becomes harder to draw.

The conversation also brings up tensions that probably won't resolve themselves quickly or cleanly. Argamon mentions the value of students practicing on each other, where future healthcare workers or social workers learn empathy by experiencing both sides of a professional interaction. A simulation can't replicate that completely, no matter how advanced it gets. He talks about instructors suddenly having access to enormous amounts of data about student behavior and not necessarily knowing what's useful versus what's just noise. He raises the concern that AI could end up interacting with AI—students using it to produce assignments, teachers using it to evaluate them—while the human connection that's supposed to be at the center of education gets squeezed out. These aren't problems with obvious solutions waiting to be implemented. They're ongoing challenges that will need continued attention as the technology develops and as institutions learn more about what actually happens when these tools move from theory into daily classroom use.

Read the full article here.

Watch it on YouTube here.

Listen on Apple Podcasts here.

Listen on Spotify here.

OTHER INTERESTING AI HIGHLIGHTS:

Google Teams Up with Internet2 to Advance AI in Higher Ed

/Google Blogs – The Keyword

Google has partnered with Internet2 to launch the AI Education Leadership Program (ELP), a nationwide initiative designed to help universities responsibly adopt AI in research, teaching, and learning. The program provides a shared framework for institutions to integrate tools like Gemini for Education and NotebookLM while building a community-driven approach to governance and implementation. Ten leading universities across the U.S. — including UC Berkeley, NYU, and the University of Washington — are joining the inaugural cohort. Together, they’ll help shape how AI enhances learning and innovation in higher education.

Another Crazy Day in AI: Teaching With and Through Technology

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