There’s a lot of talk about how AI will transform higher education. From predictive analytics and chatbots to course planning and workload models, the possibilities seem endless. But in all the excitement, it’s easy to forget one uncomfortable truth: AI is only as smart as the data we give it.
Most of the datasets we rely on in higher education weren’t designed for AI. They were built for returns, reports, and internal tracking – not for machine learning models or automated insights. Many of them reflect years of accumulated decisions, local workarounds, and legacy structures. The result is that our data tells a story, but not always the one we think it does.
The Mirror Effect
AI doesn’t invent knowledge. It mirrors the information it’s trained on, amplifying what’s already there. If the data contains gaps, inconsistencies, or biases, the system doesn’t fix them; it scales them.
Imagine an AI model designed to identify students at risk of dropping out. It learns from historical data, but that data may already reflect unequal patterns of engagement or support. Without context, the model risks reinforcing the same inequalities it was meant to address.
Or consider an AI assistant built to answer student queries. If the source data includes out-of-date course information or missing details, the system will confidently give wrong answers. That doesn’t just frustrate students, it erodes trust in the technology itself.
The Trust Factor
We often talk about AI in terms of accuracy, but trust is the real currency. If people stop believing the insights or predictions coming from AI systems, they’ll simply stop using them. That loss of confidence is much harder to repair than a technical error.
And the cause, more often than not, isn’t the AI, it’s the data behind it. Poorly defined terms. Missing values. Assumptions that made sense in one context but not another. AI doesn’t question any of it. It takes everything at face value and gives it authority.
That’s why data quality isn’t a side issue; it’s the foundation of responsible and effective AI. Without reliable data, even the smartest system will produce results that feel arbitrary or untrustworthy.
Getting Data AI-Ready
The good news is that improving data quality for AI doesn’t mean starting from scratch. It means being deliberate about what you already have.
- Check your definitions. Make sure everyone means the same thing when they say “enrolment”, “withdrawal”, or “completion”.
- Know your sources. Track where data comes from and how often it’s updated.
- Be transparent. Acknowledge the limits of what your data can tell you, and don’t overstate its accuracy.
- Involve people early. AI projects work best when data specialists, academic staff, and system owners collaborate from the start.
None of these are high-tech steps, but they’re what make high-tech tools actually work.
A Smarter Starting Point
AI in higher education has huge potential to improve insight, efficiency, and personalisation. But before we talk about artificial intelligence, we need to talk about data intelligence, the human understanding, structure, and discipline that gives AI something solid to build on.
Because in the end, AI won’t fix broken data, it will only make the cracks easier to see.
Want to have a chat about this? Contact me on LinkedIn or via email.