The State of AI Support in Higher Education: What 100,000 Conversations Reveal

Higher education has moved past the question of whether AI belongs on campus. The more pressing question in 2026 is how institutions can use AI responsibly, measurably, and at scale, without compromising academic integrity, student trust, or institutional governance.

The State of AI Support in Higher Education Report offers one of the most comprehensive answers to date. Based on nearly 100,000 real AI-powered support conversations across 24 institutions globally, the report provides rare, evidence-based insight into how AI is actually being used by students and faculty, not in theory, but in practice.

Below, we unpack the most important findings and what they mean for institutional leaders, student success teams, CIOs, and learning designers.

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For the full data analysis, takeaways, and strategic recommendations, download the complete report here.

From AI experiments to AI infrastructure on campus

One of the clearest signals from the data is a sector-wide shift away from isolated pilots.

Across regions - North America, EMEA, and APAC - AI assistants are no longer treated as “tools to try.” They are becoming core support infrastructure, embedded across admissions, academic services, learning support, and campus operations.

Institutions using AI assistants consistently reported:

• High adoption during peak academic periods
• Growing reliance during off-hours and weekends
• Increasing trust as systems proved accurate and dependable

This marks a transition from innovation to operational impact, a critical step for any institution planning AI beyond 2026.

Where students actually use AI (and why that matters)

The report reveals a clear pattern in student intent.

Nearly half of all AI interactions (46.3%) were information-seeking, focused on:

• Course logistics
• Enrollment and registration
• Academic calendars and deadlines
• Grades and transcripts

Another major share centered on academic support, including:

• Assignment scaffolding (60.9% of learning-related queries)
• Practice questions and self-testing (42.1%)
• Concept clarification and step-by-step explanations (34.4%)

What’s notable is what students are not doing: AI is not being used primarily for shortcuts or content dumping. Instead, usage trends show students increasingly treating AI as a learning partner, not a replacement for thinking.

This distinction is essential for institutions worried about academic integrity.

Academic integrity: Lower risk than expected

Despite persistent concerns about AI-enabled cheating, the data tells a different story.

Across nearly 100,000 conversations:

• Exam cheating attempts accounted for just 0.12%
• Plagiarism-related requests were only 0.06%
• Overall academic risk remained under 0.6%, even as usage scaled

Even more importantly, students generally accepted redirection when AI refused to provide direct answers. High completion rates and low abandonment suggest that clear boundaries and well-designed AI workflows reduce misuse naturally.

The takeaway for institutions is clear: Academic integrity is best protected through institutional design, policy clarity, and pedagogical alignment, not blunt technical restrictions.

24/7 support is no longer optional

One of the most striking findings relates to when students use AI. Over 41% of AI conversations occurred outside traditional office hours, including evenings, weekends, and holidays. By the end of the academic year, off-hours usage climbed to 45%.

This has clear implications for:

• Equity and access (especially for working students and international learners)
• Student anxiety around deadlines and enrollment
• Institutional staffing models

Providing round-the-clock human support at scale is financially unrealistic for most institutions. AI assistants fill this gap, offering consistent, accurate support when it would otherwise be unavailable. In practice, this reshapes what “student support” means in a global, digital-first education environment.

The operational impact institutions can actually measure

Beyond student experience, the report delivers concrete operational metrics, an area where many AI initiatives fall short.

Across participating institutions:

• Average AI resolution rate reached 97%
• Faculty and staff saved an estimated 7,348 hours
• Total operational ROI was calculated at $751,500

Time savings were driven by AI handling high-volume, repetitive inquiries, freeing staff to focus on complex, human-centered work such as advising, intervention, and academic design.

Importantly, ROI patterns varied by institutional size:

• Smaller institutions benefited most per student
• Mid-sized institutions achieved the highest efficiency
• Large institutions saw strong total ROI but require more specialized, multi-assistant strategies

This reinforces a key insight: AI success is not one-size-fits-all. Institutional context matters.

What the data reveals about regional AI priorities

Usage patterns also differed by geography.

• EMEA and APAC institutions prioritized multilingual access and broad availability, supporting an average of 10+ languages
• North American institutions focused more heavily on deeper engagement per student, with higher conversation volumes

This highlights AI’s role in global student support strategies, especially for institutions competing internationally or serving diverse learner populations.

Why AI success depends on governance, not just technology

Perhaps the most important conclusion from the report is this:

High-performing institutions did not treat AI as a standalone system. They treated it as a living part of the campus ecosystem.

The strongest outcomes were associated with:

• Deep LMS and portal integration
• Clear institutional AI guidelines
• Ongoing content and policy refinement
• Analytics-driven decision-making

Institutions with transparent expectations around AI usage saw lower misuse, higher trust, and stronger engagement, reinforcing that governance is a capability, not a constraint.

A data-backed roadmap for 2026 and beyond

The report closes with strategic guidance for institutional leaders, admissions teams, faculty, and IT leaders, outlining how AI can evolve from support automation into a continuous improvement system.

Key themes include:

• Treating AI as infrastructure, not a pilot
• Using conversation data to identify friction points
• Aligning AI with the academic calendar
• Scaling responsibly without eroding trust

For institutions planning AI adoption in 2026, the message is clear: Evidence matters. Measurement matters. And strategy matters more than experimentation.

Explore the full report

This article only scratches the surface of the insights uncovered in the State of AI Support in Higher Education Report.

The full guide includes:

• Detailed charts and benchmarks
• Regional comparisons
• ROI models by institution size
• Practical recommendations for leadership, IT, and teaching teams

Download the full State of AI Support in Higher Education Report