New Research Shows What’s Holding Higher Ed Back from Scaling AI On Campus

New research from Superhuman, in partnership with Higher Ed Dive’s Studio, examines how higher education leaders are responding to the growing need to define clear, measurable use cases for artificial intelligence (AI) on their campuses and show their impact.

The findings, which reflect responses from more than 150 higher ed leaders in the U.S., offer insight into the sector at a critical juncture. Colleges and universities are struggling to understand the impact of the AI tools and technologies they have implemented so far. As the data shows, that’s largely because those institutions aren’t prioritizing alignment with their strategic priorities when deciding which AI solutions to invest in — even though they say they want AI to support their missions. 

“This is one of the most interesting tensions in higher ed right now and demonstrates how challenging it is to roll out institution-wide AI tools and programs,” says Yolanda Wiggins, Senior Researcher at Superhuman. “While schools want their AI stack to support their strategic goals, most aren’t yet laddering up their purchasing decisions to their mission.”

The result is that institutions are struggling to build sustainable systems for AI use with shared practices that students, faculty, and staff can use and trust over time.

Key Takeaways:

• Institutions are missing opportunities to see where AI is (and isn’t) working. The data shows broad use of AI by faculty, students, and staff, with four to six use cases per group. However, nearly half (44%) of respondents said their institution’s sanctioned use of AI remains “limited” or “experimental.”
• When oversight does happen, there’s no single approach. One-quarter to one-third of respondents said the highest level of oversight of AI use is institutional (25%), departmental (34%), or at the faculty/course level (26%).
• Risk mitigation, not strategic alignment, has been the focus. Respondents ranked security-focused features higher than strategic alignment, even though the outcomes they said they hoped to achieve from AI aligned with their strategic goals. 

For example, 62% of respondents cited data privacy and security as a priority criterion when deciding whether to invest in an AI tool, whereas just 21% cited alignment with institutional strategic goals. 

There are indications that institutions are beginning to align the AI tools they onboard (and their reasons for doing so) with the mission-aligned outcomes they expect. Our findings show:

• 63% of respondents say most or all of the AI tools and platforms used on campus are managed and operated at the institutional level.
• 57% of respondents said their school has a formal policy guiding AI use across the institution. 
• 55% of respondents said AI use has been formally sanctioned in many (36%) or nearly all (19%) areas of the institution. 

What’s In the Report: 

We explore the factors holding institutions back from understanding how the AI solutions in use on campus today are affecting teaching and learning productivity. The report also explores how AI can be better harnessed to bolster campuswide teaching and learning in repeatable, scalable ways. 

We also explain how to build a business case for AI on campus, from identifying the tools or platforms most likely to make a meaningful impact to securing the buy-in needed to scale those learnings across the institution.

“When you can build AI into real institutional workflows, instead of using it ad-hoc, you’re far more likely to be able to show how AI helps save time and reduce friction, and helps everyone across campus work smarter,” Wiggins says.

Download the report to see the full findings.