Institutional Research in the AI Era: Rethinking Survey Data Workflows

Institution-wide AI adoption in higher education jumped from 49% in 2024 to 66% in 2025, according to Ellucian’s annual AI in Higher Education survey. Personal use among administrators is nearing saturation. The Digital Education Council’s 2026 global survey, drawing on more than 45,000 student and faculty responses across 35 countries, found AI use climbed another 16 percentage points year over year across both groups combined, a pattern documented in recent global higher-education research.

For an institutional research office, that gap between adoption and governance is the actual story. Faculty and students are already using AI daily, in survey design, in analyzing open-ended responses, in drafting reports. The live question is no longer whether AI touches IR work — it already does — but whether the office has a deliberate, documented position on where it’s used and where a named human owner still has to sign off.

Quick takeaways

• Institutional AI adoption in higher ed nearly reached two-thirds of institutions in 2025, up sharply from the prior year.
• Individual faculty and student AI use is outpacing institutional policy, creating a governance gap IR offices are best positioned to close.
• The clearest, lowest-risk AI use cases in IR work are mechanical — not interpretive or high-stakes.

Where AI genuinely helps IR work today

Three applications show up consistently across the research as legitimate, time-saving uses rather than hype:

• Open-ended response coding. AI can meaningfully accelerate the first pass of thematic coding on free-text survey responses, though a human still needs to validate the categories that emerge.
• Draft report generation. Turning structured survey output into a first-draft narrative report saves real time, provided a human reviews it against the raw data before it goes anywhere near accreditation or compliance use.
• Question and instrument drafting. AI-assisted survey generation can compress the time it takes to build a methodologically sound instrument from a research question — a use case QuestionPro has built directly into its academic tools.

Where the line has to hold

The consistent line institutions are drawing in 2026: AI can accelerate the mechanical parts of analysis, but sampling decisions, causal interpretation, and any output tied to accreditation, compliance, or personnel decisions need a named human owner. Ellucian’s own 2026 report notes that perceived net benefit for student-learning applications has actually softened compared to the prior year — a signal that enthusiasm is tempering into more careful, task-specific use rather than blanket adoption.

The picture is also uneven by region. Faculty intent to use AI in the US and Canada declined nine points year over year in the DEC dataset — the lowest regional trajectory recorded — even as UK student AI use continues to outpace institutional policy adoption. An IR office operating across regions or a multi-campus consortium needs a governance approach flexible enough to account for that variance, not a single global policy.

A practical adoption path

Rather than a blanket AI policy, the offices seeing the smoothest adoption tend to follow a narrower path: pilot AI on one low-stakes, high-volume task first — open-text coding is the most common starting point — document the validation process alongside it, and only expand to higher-stakes applications once that first workflow has a track record. QuestionPro’s research suite is built to support exactly this kind of staged rollout, with AI-assisted analysis layered onto, not replacing, the underlying methodology controls IR teams already rely on.

Frequently asked questions

Is AI adoption in higher ed IR offices actually mainstream yet, or still experimental?

Institution-wide adoption crossed 66% in 2025 per Ellucian’s survey, which suggests it’s mainstream at the institutional level — but governance and documented policy are lagging behind actual use, which is the real risk for IR offices specifically.

What’s the safest place to start using AI in institutional research?

Open-ended response coding is the most commonly cited low-risk starting point — it’s high-volume, time-consuming manually, and easy to validate against a human-coded sample before scaling up.