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Universities worldwide are racing to adopt AI across teaching, research and operations. While many report that these tools have been successfully integrated, researchers at the Institute of Learning within the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) in the United Arab Emirates have identified a deeper pattern beneath the surface of AI literacy.

Nabil Zary, senior director of MBRU’s Institute of Learning, points out a growing deficit he calls organisational learning debt. Zary and his team coined the term to describe when a higher education institution deploys AI tools faster than it develops the capacity to use, evaluate and govern them. As with financial debt, the longer you leave it, the more it compounds.

Zary is investigating the problems created by this debt and why the conversation around AI readiness needs to be reframed. “For me, an AI-literate institution is not one where everyone has completed a training course,” says Zary. “It is one in which four things develop together: the capabilities of people; the infrastructure that enables them to use AI effectively; a culture that rewards critically analysing AI outputs; and governance that keeps pace with deployment. Most universities have invested heavily in the first and barely touched the other three.”

Instead of focusing mainly on which AI tools to use, university leaders should ask whether they are accumulating learning debt as they integrate those tools. Although this may not appear in annual reports, it is liable to show up eventually.

“Most universities will tell you they have adopted AI,” Zary says. “They have run workshops, bought licences and updated their policies. However, the approach at most institutions is to treat AI readiness as a training problem. But our analysis indicates issues stretch across dimensions such as capability, infrastructure, culture and governance. If you only address one, the gains from individual training don’t translate into institutional readiness.”

MBRU is part of Dubai Health,an integrated network that connects hospitals, medical education and research to drive healthcare excellence and innovation in Dubai.

“Our academic council identified that AI tools were being used widely across the university without consistent guidance or governance,” explains Zary. “So we decided to build the infrastructure to do it properly. We then formalised it into a set of testable propositions anchored in our AI literacy framework. We also developed assessment instruments to measure learning debt across the four dimensions, before launching an empirical validation programme. The concept and the framework are explained in a paper currently under peer review.”

Established as a benchmark rather than a training programme, MBRU’s AI literacy framework is designed to become part of how the institution operates and is connected directly to its governance. It covers five core competencies: technical understanding, critical evaluation, practical application, ethical reasoning and data literacy. MBRU has embedded the framework within its institutional policy, governance structures, assessment and training pathways.

“The first step in becoming AI-ready is carrying out an honest assessment of your learning debt across all four dimensions, and then investing in infrastructure, culture and governance alongside training,” Zary says. “We have made the AI literacy framework available under an open licence because this problem isn’t unique to us. Every university and health system deploying AI is facing some version of this challenge. We are keen to see other institutions test these ideas in their own contexts.”