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A Detailed Breakdown of Artificial Intelligence in Chatbots

Over the past several years, businesses have rushed to implement this technology to streamline customer communication. However, many chatbot experiences leave much to be desired, especially within higher education.

DIY chatbots fail to deal with more nuanced questions and require a large amount of conversational data to train. They can also struggle to handle topics outside of the few areas for which they’ve been trained.

Innovative higher ed institutions are buying chatbots powered with artificial intelligence so they are better equipped to handle student questions on a variety of topics ranging from financial aid to student housing questions, and everything in between. The key to successful AI implementation is that it must understand the student’s request, possess highly specific knowledge and deliver a personalized experience.

Here are a few ways AI currently impacts chatbots:

Cognitive Thinking – This includes the chatbot’s ability to interpret various student questions and pull from previous interactions, in addition to other pieces of information to provide contextually relevant information.

Memory – AI helps a chatbot remember relevant details to assist students throughout a conversation. It can also store information from a previous conversation so that users can provide less information to get the answers they need.

Persistence – In higher education, maintaining student deadlines is a big task for higher ed administrators across the country. AI technology helps users pick up a conversation where they left off, even on a different device, and lets bots follow up with students when deadlines are fast approaching.

Topic Switching – Allows users to go off-topic without throwing the bot off track. An AI chatbot can follow a student switching from something like university housing information to course selection, and even bring him or her back on track if the bot lacks an answer it’s looking for.

Personalization and Personality – The advent of artificial intelligence allows humans to converse with bots casually using humor, empathy, sarcasm or a wide range of emotions. In addition, it enables the bot to understand which inputs of information can answer specific questions to deliver a more personalized experience.

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Breaking Down the Technology of AI Chatbots
In order to fully leverage AI chatbots, it’s crucial to understand some of the technology used to help it outperform DIYl chatbots. Artificial intelligence helps the bot understand language, and learn over time.

The chatbot receives data and interprets, contextualizes and translates it so that it can provide the appropriate answer when prompted.

At a more granular level, here are some different techniques AI chatbots use to improve performance.

  • Machine Learning – The ability for the system to improve functionality based on a variety of algorithms including pattern and text recognition. Over time, as it has more reference data, the machine learns to become more efficient.


  • Natural-language Processing – A process that deals with a bot’s ability to analyze language through speech recognition, semantics and syntax. Just like a human learns a language through listening and reading while understanding the context, computers can attain a similar capability.


  • Deep Learning – A broader version of machine learning, deep learning is the ability for a computer to process various pieces of information the way a human would to make informed decisions and judgements. Deep learning uses neural networks to prioritize data by assigning a numerical value to each data point or using true/false logic analysis.
  • Neural Networks – Neural networks refer to the vast clusters of data within a computer system that leverage their proximity to other related clusters of data to increase each cluster’s ability to learn from the other, much the way the human brain and nervous system do.


  • Support Vector Machines (SVM) – SVM technology allows machines to identify optimal solutions when faced with multiple options. Machines are typically fed a small set of data samples to help it find an optimal solution.


  • Supervised/unsupervised Learning – Machine learning often contains three types of learning: supervised learning, semi-supervised and unsupervised learning. In supervised learning, the machine’s output that it is supposed to learn for understanding rules are given as ground-truth during training. For unsupervised learning, the intended answers, or equivalently machine output, is not provided. Any rules or inferences the machine learns are determined strictly using machine learning algorithms, independent of having been provided the answers beforehand. Semi-supervised learning falls between structured and unstructured learning.

While many researchers hold out hope for a completely intelligent chatbot that can talk to a human in the same manner as a live agent, AI technology is not at the point where this desire is feasible.

Instead, chatbots are best utilized when there are predetermined topics for which the bot can gain expertise and address before passing more sophisticated topics to a human. However, we can expect that in the coming years, that gap will continue to shrink as bots grow the capacity to handle more complex decision-making capabilities.

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