At the EDUCAUSE annual conference in Nashville this week, the nonprofit's President and CEO John O’Brien said this year’s list involved work from 58 volunteers from 26 campuses over the course of 10 months, evolving through feedback in the form of interviews, panels, discussions and ultimately a vote from members.
In a breakout session, presenters separated the 10 priorities into two categories: those focused on strengthening collective will, and those focused on building individual capabilities. They said the work of higher-ed technology leaders to de-silo data and share governance can help boost the will of the collective better than traditional committee work, and the top priority on EDUCAUSE's list this year applied this concept to cybersecurity.
1: Collaborative cybersecurity
As various technologies grow more integrated across a campus, the institution needs a more connected and collaborative approach to keep them safe, Mark McCormack, senior director of analytics and research at EDUCAUSE, said. This involves considering the user experience for a variety of campus populations.
“It’s really important to build that culture and to really have that conversation about why these changes are important, how they impact you and why it’s not just another thing that slows them down to getting their work done,” Fred Kass, associate chief information officer at Amherst College, said in a video at the conference presentation.
2: The human edge of AI
Three years after the big boom of generative AI, McCormack said, focus on the technology has shifted away from enabling institutional capacities and toward human-level interactions, including empowering students and faculty to create their own tools. The small-scale focus can drive large-scale efforts, he said — for example, the specifics of how students are using AI can impact subsequent AI training and literacy efforts.
3: Data analytics for operational and financial insights
Given outside pressures like federal funding cuts, wavering public trust in higher ed and the impending enrollment cliff, the potential for data analytics to find areas to scale back is becoming increasingly important. Institutions are taking stock of their technology tools and ensuring they are equipped to step into the next wave of AI, as well.
“Are we having the right tools, and are we having modernized technology to be able to support emerging trends like AI and other things that are happening currently that's disrupting higher ed?” Hema Manickavinayaham, digital transformation leader at California State University, Sacramento, said. “Are we a data-ready institute?”
4: Building a data-centric culture across the institution
Beyond incorporating data into decision-making at the financial level, EDUCAUSE members found a growing focus on building a “data culture” at higher ed institutions, which they say needs to start with buy-in at the highest level.
For example, Helen Norris, recently retired CIO of Chapman University in California, said Chapman’s provost sponsored major data initiatives, even putting “got data?” in her email signature. This made data use common and helped address attrition, she said.
5: Knowledge management for safer AI
The fifth point on EDUCAUSE's list focuses on the back end of AI tools. Enterprise AI tools are only as good as the data they’re fed, McCormack said, and leaders are focused on mitigating the garbage-in-garbage-out effect. The definition of “garbage” in this context has expanded, as well.
“If the data you’re inputting are outdated or incomplete or overly complex, it can lead to inaccurate AI outputs and erode user trust in those tools,” McCormack said. “So, technology leaders are focused on knowledge management to help ensure their AI solutions know the right things about their institution.”
6: Measured approaches to new technologies
McCormack said the ed-tech market’s evolution this year showed no signs of slowing down, and campuses are feeling the pressure to keep up. However, he cautioned that falling behind can lead to technical debt, poor interoperability and strain on technology staff. In light of this, tech leaders are homing in on what factors indicate a strong return on investment for each purchase, and also considering when it’s best to wait for the next iteration of a product, stick with what they already have and focus on small-scale pilots instead of major investments in the next big thing.
7: Technology literacy for the future workforce
On the student side, colleges and universities are increasingly focused on aligning education to workforce expectations of technology skills. Incorporating both tools and training into education is becoming more commonplace.
“We're pretty much putting [AI] everywhere,” Jesus Ramirez, director of student products at the University of California, Los Angeles (UCLA), said in a video.
For example, the AI licenses that UCLA grants students follow them after graduation, Ramirez said, to help them transition into their careers.
8: From reactive to proactive
As the rate of technological and cultural change increases, higher-ed leaders are using data and technology tools to try to forecast the future. McCormack said schools already use analytics tools on grade and attendance logs to target student support, and the use of predictive analytics is expanding to recruitment, retention and business models.
9: AI-enabled efficiencies and growth
In addition to using AI tools to identify budget-trimming opportunities, schools are using AI for its primary promise: boosting efficiency. McCormack said change management and quick, low-risk wins like data analytics are common focuses for using AI to improve workflows.
10: Decision-maker data skills and literacy
Finally, institutions are ensuring their decision-makers have key data skills in which they can then help train their staff.
“We have so much data in higher ed, and increasingly powerful analytics, but all of it means very little if decision-makers don’t understand, know how to use or trust the data,” McCormack said.
