AI is becoming more common in higher education.
From content generation to analytics, institutions are starting to explore how these tools can support teaching, learning, and decision-making. There is real excitement around what AI can enable, especially when it comes to working with large amounts of data.
At the same time, there is a growing concern.
Can we trust what AI is telling us?
AI hallucinations occur when a system generates information that sounds accurate, but is not fully grounded in the underlying data.
In qualitative analysis, this can show up in subtle ways. A model might assign the wrong theme to a response or overstate a pattern that is not actually there. It may generate summaries that feel convincing but do not fully reflect what was said. In some cases, it may even introduce phrasing that was never explicitly present in the original feedback.
These issues are not always obvious, which makes them difficult to catch.
Qualitative data is already complex. It carries tone, context, intent, and nuance. Unlike structured data, it does not have a single clear interpretation.
When AI misinterprets that data, the impact is not just technical. It affects how people are understood.
A student asking for help could be overlooked. A concern could be misclassified. A pattern could be misunderstood. Over time, these small gaps can lead to larger issues in how feedback is interpreted and acted on.
Trust becomes a challenge when insights cannot be clearly explained.
If an educator sees a theme or a summary but cannot trace it back to the original responses, it becomes difficult to rely on it. The output may look polished, but the reasoning behind it is unclear.
There are also broader concerns around governance and privacy. As more data is processed through AI systems, questions arise around how that data is handled, who has access to it, and how decisions are being made.
In many cases, the issue is not whether AI is useful. It is whether it is understandable.
There is a tendency to view AI as a fully automated solution.
But qualitative analysis has never worked that way. It has always required interpretation, reflection, and human judgment.
When AI is used without oversight, it can flatten meaning, miss nuance, or introduce unintended bias. It can move quickly, but not always accurately.
This does not mean AI should not be used. It means it should be used with care.
A more effective approach combines AI with human oversight.
AI can help organize large volumes of feedback, surface patterns, and highlight areas that may need attention. It can make the process faster and more manageable.
At the same time, human judgment remains essential. Interpretation, validation, and decision-making still rely on context and experience.
This hybrid approach allows for both scale and accuracy. It also creates space for transparency, where insights can be reviewed and trusted
As AI continues to evolve, trust will play a central role in how it is adopted.
Institutions are not just looking for faster tools. They are looking for tools they can rely on. That means insights that can be explained, outputs that can be connected back to real data, and systems that respect privacy and governance requirements.
Most importantly, it means recognizing that human input cannot be reduced to simple outputs without losing something important.
At Feedback Fusion, we think a lot about this balance.
Not just how to use AI, but how to use it responsibly. That means focusing on transparency, preserving context, and ensuring that insights are grounded in real responses.
It also means keeping humans in the loop.
Because the goal is not just to analyze feedback. It is to understand it well enough to act on it with confidence.
We are continuing to explore how AI can support qualitative analysis in a way that is both scalable and trustworthy.
If you are thinking about these challenges too, we would love to connect.