Prospective students are no longer only manually comparing university websites. They are asking ChatGPT, Google, Gemini and other LLMs specific questions to help them figure out which choice makes most sense to them.
That makes the role of student reviews more important. A review is no longer only social proof to a human reader. It is also credible evidence that AI systems will use when they answer questions like:
- Is this specific programme a good fit for me?
- What do students like or dislike about this school?
- What are the pros and cons of this institution compared to my other options?
In a recent discussion with Kris Achten from KdG University of Applied Sciences and Arts and Jordi Robert-Ribes from EDUopinions, four ideas stood out. They are worth your time even if you never watch the full conversation.
1. Good reviews prove you deliver on your promise
KdG did not start asking their students for reviews in year 1, when they had just started their English-taught programmes.
They first got clear about what they offered, who they were for, and what kind of student experience they wanted to create. And they did this in collaboration with their students. In the first years, they ran surveys.
They only started proactively asking for reviews when they noticed that their current students started referring other students to them who had exactly the profile that was a good fit for KdG: a non-prestigious, inclusive university with an applied focus.
If you’re promoting your school in one way, but student reviews say something else, the gap will become apparent. But if the two are aligned, reviews become proof that you actually deliver what you claim.
Not that you are “the best university”. That is meaningless. But that you are a good fit for a specific kind of student.
2. The review content matters more than five stars
Kris compared this with restaurants. McDonald’s can get five-star reviews. A Michelin-star restaurant can get mediocre or downright negative reviews from people who expected something else.
It’s all about people’s expectations up front. And it works the same way in education. A five-star review that says “great teachers, nice campus, good experience” is pleasant. But the information gain from that, for students and LLMs, is minimal.
A useful framework for reviews is story and transformation. It’s about the student’s own experience, not about what they think about the school:
- Before I joined, I was worried about this
- During the programme, this helped me
- Now I can do something that I could not do before
Ideally, the “before” talks about what prospective students are worried about. The “after” speaks to prospective students’ aspirations.
It’s not natural for students to give this kind of testimonial. But you can prompt them without telling them what to say.
Graduation ceremonies are a great moment to ask for reviews, especially if you ask graduating students to think back to who they were when they started, and what changed during their time as a student.
3. A three-star review is not always bad
Many institutions are afraid of reviews because they fear imperfect ones. Jordi pushed back on that. A three-star review can still say useful things. It may mention what worked, what was difficult, and who the programme is best suited for.
For a long time, our job as marketers has been to present an image that’s as polished and positive as possible for the institutions we represent.
With AI tools at their disposal, that no longer works.
Students are going to discover the good, the bad and the ugly about your school anyway. Our role as marketers needs to change to making sure that answers to students’ questions and concerns are out there to discover, on our website and beyond.
Make sure AI systems have plenty of evidence to discover. And where a certain review is unfair or things have changed, make sure the appropriate context is available too, whether on your website or in a response on the review website.
4. If you do not explain who you are for, AI may do it for you
Kris and KdG are very proactive in also addressing people who are likely not a good fit for the institution. KdG has defined negative personas to this end.
For example, if someone is mainly looking for prestige and exclusivity, KdG doesn’t fit that profile. KdG is rather the opposite of that and is built around inclusivity, support and applied learning.
They address these negative personas explicitly in their website copy and in email templates that can be sent to students who are obviously not a good fit.
The interesting thing is that if you have a rich and representative body of externally hosted reviews, AI systems can infer your negative personas for you and advise prospective students to look elsewhere.
That way, everyone wins: good-fit students end up at your school, and students who are a bad fit find out quickly and can find a place that is a better match to their expectations.
How to get started with this
If you want to get to work with this, we each prepared something useful to go with the session:
- Jordi: a practical playbook for collecting honest reviews that are useful for AI and future students.
- Kris: an outsider perspective on your messaging, brand, and reputation.
- Me: a free AI visibility report, where I check what different AI tools say about your school over two weeks, and what you can do to improve how you show up.
You can request the resources here: https://guusgoorts.com/get
This article is adapted from Genuinely Helpful – Field Notes, Guus Goorts’ newsletter for education marketers. Want more practical notes like this? You can subscribe to Genuinely Helpful – Field Notes.
