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About the Episode: 

In this special live episode recorded live from the AI Show at ASU+GSV, Dustin sits down with Dr. Shalini Gopalkrishnan—AI strategist at San Francisco Bay University—for a thought-provoking and practical look at how AI is reshaping the university experience. From tutoring and enrollment to mental health support and empathy-driven VR, Shalini shares how she’s embedding AI into every corner of the campus ecosystem.

This conversation covers both the bold possibilities and the essential guardrails needed as higher ed steps into a rapidly evolving AI landscape. It’s a call to reimagine—not just digitize—the future of college.

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What Is Student Feedback Analysis?

Student feedback—via course evaluations, surveys, and open-ended reflections—has long been essential in higher ed. It informs what’s working in courses, highlights pain points in student services, and reveals gaps in learning support. But traditional analysis methods are often slow, inconsistent, and limited in scope.

This is where AI steps in. AI tools can process vast volumes of feedback from multiple departments and campuses, identifying patterns and surfacing student concerns in real time. Whether it’s a drop in satisfaction with an instructor or recurring issues in a course’s design, AI turns qualitative feedback into actionable insight.

And feedback analysis is just one piece of the larger puzzle. According to Dr. Gopalkrishnan, everything in the university experience—course content, advising, admissions, scheduling, research support—is being reimagined with AI in the loop.

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The Role of AI in Modern Feedback and Campus Operations

At San Francisco Bay University, AI isn’t an add-on—it’s woven into nearly every part of the student experience. As Dr. Gopalkrishnan explains, AI tutors are already commonplace, but SFBU is also exploring AI at the reception desk, in course scheduling, and even in helping students launch businesses in under an hour using AI tools.

Across higher ed, AI is enabling a more personalized, responsive campus. For instance, chatbots can triage student questions, recommend courses, or offer wellness support. Enrollment teams can use AI to anticipate application surges, while advisors get nudges when students might be falling behind.

The key? Values-driven implementation. Institutions don’t need to do everything at once. They can start small—introducing AI tutors in high-enrollment courses or experimenting with smart scheduling tools—and scale from there.

Natural Language Processing (NLP): Making Student Voices Heard

Natural Language Processing (NLP) allows AI to understand and categorize human language—transforming free-text feedback into structured insights. With NLP, schools can detect patterns like recurring frustrations in course pacing or praise for a particularly impactful professor.

This technology is especially powerful in surfacing voices that might otherwise go unheard. For example, students who leave long, thoughtful comments in surveys often provide the richest data—but it’s historically the hardest to analyze at scale.

By automating this analysis, institutions gain a clearer, more comprehensive picture of how students are experiencing their education—and where improvements are most urgently needed.

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Sentiment Analysis: Reading Between the Lines

AI doesn’t just read what students say—it senses how they feel. Sentiment analysis decodes tone, emotion, and intent behind student feedback. Whether a student says, “This course is tough” with admiration or frustration, AI helps institutions tell the difference.

This matters. Emotional cues embedded in language offer early warning signs about course satisfaction, faculty effectiveness, or campus climate. Sentiment analysis enables real-time intervention, such as flagging when students might benefit from additional support or outreach.

For institutions focused on higher ed trends like retention and wellness, this insight is gold. It’s not just what students say—it’s how they say it that reveals what’s working and what needs fixing.

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Machine Learning and Pattern Recognition: Predicting What’s Next

Machine learning takes feedback analysis a step further by identifying patterns over time. Dr. Gopalkrishnan notes that AI models can detect shifts in sentiment across semesters, spot rising issues in particular departments, and even forecast potential roadblocks for students.

These systems get smarter as they learn. The more feedback they process, the better they become at recognizing the subtle signals that precede larger challenges—like disengagement, burnout, or a poorly designed syllabus.

In short, AI turns student feedback from a post-mortem into a real-time strategy tool.

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Benefits of AI in Student Feedback and University Life

The adoption of AI tools brings several powerful advantages:

• Faster and Scalable Analysis: AI can process thousands of survey responses in minutes—freeing up staff to focus on strategy, not sorting through spreadsheets.
• Actionable Insights in Real-Time: AI surfaces issues as they arise, enabling mid-course corrections, targeted interventions, and more agile decision-making.
• Reduced Human Bias: AI offers a more consistent lens on student sentiment, reducing the risk of subjective interpretation and ensuring fairer outcomes.

As institutions strive to measure and improve student success, these capabilities are proving invaluable—especially in large, diverse, or multi-campus settings.

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Best Practices for Implementing AI in Higher Ed

Dr. Gopalkrishnan emphasizes that AI should be implemented with empathy, transparency, and purpose. Here’s how institutions can get started:

• Choose the Right Tools: Select AI platforms tailored for education, with intuitive dashboards, flexible integration, and clear data privacy practices. The best ed tech tools are those that are easy to use and easy to trust.
• Blend Quantitative and Qualitative Data: Use AI to complement—not replace—human insight. Combine feedback trends with course grades, completion rates, and student interviews for a 360-degree view.
• Train Faculty and Staff: Empower everyone on campus to use AI insights confidently and responsibly. Build training programs that explain not just how to use AI tools, but why they matter.

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The Future of Universities in the Age of AI

Looking ahead, Dr. Gopalkrishnan sees two possible futures—one where universities evolve incrementally, and another where they are completely reimagined. At the edge of this evolution are AI-powered campuses with no faculty, AI-built courses, and virtual learning environments customized for every student.

But she also sees a critical role for community. “Universities shouldn’t just train workers,” she says. “They should build citizens.” Her vision includes institutions as hubs for social change—teaching empathy through virtual reality, supporting at-risk communities, and empowering students to co-create their learning journeys.

Ultimately, the most promising future for AI in education is human-centered. “We’re not going down,” she adds. “We’re just becoming more thoughtful and efficient.”

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About the Show: The Higher Ed Geek Podcast explores the impact of edtech on the student experience by speaking with diverse leaders from institutions, companies, and nonprofit organizations. Each week we aim to provide an engaging, fun, and relevant dose of professional development that honors the wide range of work happening all across the higher ed ecosystem. Come geek out with us! The Higher Ed Geek Podcast is hosted by Dustin Ramsdell and is a proud member of the Enrollify Podcast Network.

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Connect With Our Host:
Dustin Ramsdell‍

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About The Enrollify Podcast Network:
The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!  

Some of our favorites include Generation AI and  I Wanna Work There. 

Enrollify is produced by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.

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