Key Takeaways:
- Human-first Automation: Freeing faculty from tasks to prioritize mentorship and connection.
- Redefined Engagement: Students want flexible, digital-first pathways—with human support when needed.
- Lifelong Learning Model: AI empowers institutions to become partners across a learner’s lifetime.
Insights from the Instructure Roundtable at EDUCAUSE 2025
At this year’s EDUCAUSE conference, Instructure hosted a roundtable exploring one of higher education’s most pressing questions:
- How is AI reshaping education?
- What it means to teach and learn?
- How can institutions ensure innovation strengthens, rather than replaces, the human connection?
I was the moderator of the discussion that brought together leaders from Arizona State University (ASU), Indiana University (IU), Liberty University, Columbia University, and Instructure. Together, we examined how artificial intelligence is influencing pedagogy, student engagement, and institutional readiness—and what that means for the future of higher education.
Below are some of the most thought-provoking ideas shared during the conversation. These insights not only reflect current experimentation with AI but also chart a path forward for institutions committed to preserving the human core of higher education.
AI as a Catalyst for Connection
Rather than viewing AI as a threat, participants saw it as an opportunity to reclaim time for human relationships.
“AI can take some of the laborious tasks off our plate and let us focus on connecting with students,” one participant said. Many agreed that automation could reduce administrative burdens, allowing faculty to spend more time mentoring, advising, and supporting learners.
Still, this evolution comes with challenges. “Students feel the inconsistency—one class allows AI, another forbids it,” another added. “We need to think about the student journey, not just faculty preference.”
Student Expectations Are Changing
Panelists noted that today’s students expect seamless, digital-first experiences, often preferring asynchronous or self-service interactions over traditional face-to-face meetings. “Students don’t want to pick up the phone or walk into an office—they just want it figured out for them,” one attendee remarked.
That shift doesn’t eliminate the need for human connection—it redefines it. The discussion highlighted how new learning platforms are evolving to support personalization at scale, giving students more control over how and when they engage with content.
Closing the Skills Gap
The group turned to one of higher education’s toughest challenges: aligning academic learning with workforce needs. “We don’t even know what the future workforce will look like,” one speaker noted. “Even if we moved fast, the pavement isn’t laid yet.”
Still, some institutions are taking bold steps. Indiana University launched GenAI 101, a free course designed to introduce AI concepts to students, staff, and alumni. Within weeks, more than 100,000 people had enrolled, and tens of thousands completed modules to earn a digital badge.
“Our alumni were asking for access before we even announced it,” said one IU representative. “It showed how hungry people are to understand AI in practical terms.”
Badging, Microcredentials, and the Meaning of Proof
The conversation also explored the growing role of digital credentials and microcredentials. ASU’s Create AI Builders program, for instance, allows faculty to design AI-powered teaching tools and track student engagement, while Columbia and other universities are testing new frameworks to certify AI literacy.
However, participants acknowledged that the sector still lacks a unified standard. “Right now, most badges say more about the issuing institution than the learner,” one said. “Until we agree on what skills are being measured, the value will remain inconsistent.”
Others pointed out that microcredentials can help sustain motivation: “It’s not always about employer recognition—sometimes it’s about giving learners a visible sense of progress.”
Institutional Readiness and Equity
AI adoption remains uneven across institutions. Larger universities with dedicated design teams and budgets tend to advance faster than smaller schools.
At ASU, a transparent, community-driven model encourages faculty to share experiences using AI, fostering faster innovation. “When faculty can see each other’s experiments, adoption moves faster,” one participant said.
Several panelists also emphasized partnerships between institutions and technology providers as essential to leveling the playing field. “We’ll always have different budgets,” one observed, “but we can still share what works.”
Rethinking Higher Education’s Mission
The discussion concluded with a broader reflection: the potential for universities to become lifelong learning partners rather than one-time degree providers.
With 70% of U.S. workers saying they feel unprepared for today’s workforce (according to Instructure’s latest report), the panel agreed that higher education must evolve into a continuous learning ecosystem. Initiatives like IU’s open-access AI literacy course offer a glimpse of what that could look like.
“We’ve been talking about lifelong learning for years,” one participant said. “Now the technology and the need are finally converging.”
Core Insight
AI is not replacing higher education’s mission—it’s reframing it.
From automating administrative tasks to enabling continuous learning, AI gives institutions tools to make education more personal, adaptive, and human. The challenge isn’t whether to use AI—it’s how to use it to enhance what makes education distinctively human.
As one participant concluded,
“Education is still about helping people learn to think, to create, and to connect. AI just gives us new ways to do it.”
