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How User Ratings Improve Chatbots in Higher Education

We’ve all realized the benefits of artificial intelligence at one point or another. According to Forbes, chatbots help users get answers to their questions on demand, without the need to wait for an available representative. customers benefit from transparent user ratings that help refine and improve their service delivery. In a recent study conducted at Indiana University, researchers found that IU students not only preferred using their chatbot to their traditional self-help tool, but it was also easier, and they were more likely to use it for future questions.

But not all chatbots are the same. The user experience can vary from one provider to the next. Some chatbots understand natural language questions, context, unique content, and more, while others rely on general information and user-driven flows. Ultimately, some chatbots will deliver helpful information while others may only frustrate users.

This underscores the need for transparent user ratings following each interaction. Providing users with the opportunity to rate their experience is essential to delivering improved customer service. Without understanding the pitfalls of your service delivery, it’s near impossible to improve it.

How to Get User Feedback

One method for securing user feedback is randomized solicitation. Chatbots can be configured to randomly select users and request their feedback using a form. The forms can be set up with closed questions to evaluate whether the user found the information they were seeking, if the experience was easy, and more.

The downside to randomized solicitation is that it can be difficult to facilitate participation in surveys. It’s also difficult to pinpoint when the chatbot should prompt the user to provide feedback. Additionally, while randomization helps eliminate bias, it only captures a segment of the population’s feedback.

For this reason, chatbots deliver a simple, non-invasive question following each user interaction. Users are prompted to indicate whether or not the returned content was helpful by selecting either a smile or a frown. This provides the user with the flexibility to provide feedback for every use-case. For example, perhaps the chatbot is helpful for finding information about financial aid deadlines, but falls short when assisting users with support for meal plans.

While most chatbot implementations have a focused scope (i.e. they help with one functional area or department), chatbots are equipped to support any department in higher education. Allowing the user to rate their experience on each question provides better visibility into the chatbot’s strengths and weaknesses, as well as the users’ expectations.

How to Use Chat Feedback customers automatically receive alerts for all negative feedback. This helps to isolate functional deficits, confusing content, and potential subject areas for knowledge expansion. This provides administrators with powerful intel that helps shape decisions that improve service delivery.

In an ideal world, a company’s customer service is exceptional, and their users would never provide feedback that isn’t positive in nature. However, we know that gaps exist – the key is understanding where they exist, and what you do with that information.

To that end, negative ratings are not a bad thing. In fact, they’re incredibly helpful. They offer direct, succinct instruction to administrators that the service delivery can be improved. This enables them to update the chatbot’s knowledge, revise its message delivery, modify website content, engage in proactive communication campaigns, and many other activities that ultimately enhance customer service. Of course, such initiatives might be misdirected without the guidance of user feedback.

Here’s an example of chatbot knowledge that was modified following a negative user rating.

Why Some Chatbots Don’t Support User Ratings

As mentioned previously, not all chatbots are alike. From a technical perspective, it’s incredibly simple to implement a mechanism to support user feedback. So, why is it that some chatbots fail to capitalize on such valuable insights?

It all comes down to actionable data. Negative user feedback is only useful when it’s actionable. That is, if an administrator is unable to improve the chatbot’s service delivery, then knowledge of its shortcomings only emphasizes the inability of the chatbot to satisfy users.

When a chatbot isn’t built to understand natural language questions, context, and your unique content, it’s inherently less capable of providing your users with the information they need. If a provider doesn’t deliver insight into how its chatbot can be improved, it’s safe to expect that at best its deployment will only marginally improve the service experience. More importantly, should you determine which aspects of the chatbot require improvement, it’s unlikely that you’ll have the means to make those improvements.

Regardless of why a chatbot provider elects not to mobilize user feedback, it’s impossible to ignore the benefit of transparent user ratings.’s chatbots leverage user ratings to deliver an enhanced experience for your users, and in turn, for you. To learn more about chatbots from, go to or click here to schedule a free consultation.