Higher-ed leaders and educators at colleges and universities don’t have the luxury of sitting back to wait and see how AI shakes out.

An AI primer for higher-ed leaders and educators


Colleges and universities don’t have the luxury of sitting back to wait and see how AI shakes out

Key points:

While nearly every sector touts AI’s potential, it’s important to first understand what AI is before outlining how it can revolutionize any given industry. The same is true in higher education, notes an AI primer from the Partnership for Education Advancement (Ed Advancement).

The primer offers an overview of what higher-education leaders must know about how AI might reshape the way institutions teach, support, and operate. It also offers case studies of how HBCUs are using emerging technology to support institutional objectives.

Ed Advancement is a nonprofit that offers scalable and sustainable operations- and technology-focused solutions to HBCUs to help them serve their students and meet their strategic goals.

When it comes to colleges and universities, AI is poised to dramatically improve administrative process along with equity and access to higher learning itself. Not a single platform or tool, AI is an encompassing technology that is increasingly woven into myriad platforms and tools higher-ed faculty, administrators, and students use daily.

“Against this backdrop, institutions–particularly those that have been historically underfunded–face the daunting task of considering how ever-changing AI technology will alter what needs to be taught, how it will be taught and how student expectations will change,” writes Ed Advancement CEO James Runcie.

“At this critical inflection point in AI adoption, it is important that historically underfunded institutions and historically marginalized learners be at the forefront. These institutions and learners may have the most to gain from the power of AI tools to advance opportunities, but they also risk facing a wider resource gap and other potential harms (e.g., algorithmic bias and discrimination and skills and opportunity gaps) if AI isn’t developed and disseminated in an equitable way.”

When it comes to higher education, particularly under-resourced institutions, AI-enabled tools are categorized into two main opportunities:

1. Leverage automation for cost savings and improved outcomes: By automating tasks and creating efficiencies or by better identifying the highest-value approaches, AI can help institutions with limited resources free up capacity for higher-order activities or make the most of limited budgets.

2. Leapfrog to enable data-driven decision making: For institutions that haven’t had the resources to prioritize investments in IT infrastructure over the last decade, the rise of AI represents a moment to leapfrog past institutions that have adopted previous generations of IT solutions. For example, multi system insights (e.g., algorithms that pull from both the student information system and learning management system) were previously only available to institutions with a robust data warehouse and data science team. Today, AI built on top of data lakes (which utilizes unstructured data versus categorized/mapped data in a data warehouse) provides a lower-cost option to garner similar insights.

There are four areas under which AI use in higher education generally falls:

  • Support for teaching and learning: Intelligent tutoring, accessibility and scaffolding, and content generation
  • Driving student success: Improved prediction for targeted supports, insights for personalized learning paths, and nudges to stay organized and engaged
  • Informing operational improvement: Enrollment management improvement, improvements in student experience, and course improvement
  • Supporting skills documentation: Skills mapping to translate course content to in-demand workforce skills, and transcript review and resume creation

Despite its promise, AI does have the potential to widen equity gaps–a concern that’s among the main drivers behind the AI primer, which can help connect HBCUs to examples of how to use AI to achieve institutional objectives.

Concerns around these gaps include AI’s bias and disparate impact, cost and equitable access, and implementation best practices.

So, what’s next in the quest for AI-based institutional improvement? Not acting at all will be detrimental to institutions, because students will expect access to AI-powered tools as well as the ability to develop their own AI skills for workforce success.

A wise first step, the primer asserts, is to find a way to use AI to support one of the institution’s priorities. A deeper dive into AI integration means taking a look at data structure, governance, and security.

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Laura Ascione

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