The goal of most AI is to pass a Turing test. This means it can pass as a human more than 30% of the time. When it comes to online chat, a great chatbot will make the digital transformation between AI customer support and human customer support seamless.

However, the artificial intelligence behind chatbots and most customer support software, even bots on pages like Reddit and Facebook, can seem human in a limited capacity by being limited. They have certain programmed conditions and responses and play a type of Marco-Polo game. There is no thinking involved, though they have been made to appear as if there is.

I avoided contact with chatbots for the longest time. How quickly they could come back with a reply was unnerving and often what they decided was best for me was not what I wanted at all. Several years later things began to change. Chatbots were being used to collect the basic amount of data around a query and then this was passed off to a human representative. The bot’s behavior was smoothed out so there as little, if any, noticeable difference during the transition. This is where the true leverage of AI system can be found, taking all of the information gathering and tedium out of customer service work.

The following tactics can be used to program an artificial intelligence AI that has a better chance of being read as human. Knowing these tactics can be helpful if you’re setting up a chatbot. Knowing how an AI typically works can also help you get where you want to be if you have to deal with a less-than-effective chatbot. 

Output Delay

People can’t type instantaneously or read, interpret, and type a response within seconds of seeing it. Machine learnings can. Often, chatbots are programmed to allow for a small, 1 to 2-second delay before displaying a message on the screen. 

Reading this for the first time, you might think this is an inefficient quirk. However, people respond positively to these delays. Immediate responses can be unsettling and are often rated as cold. A delay, often accompanied by a “typing symbol” or animation is more likely to be rated as human or human-like.


Keywords and Context

The reductionist approach takes complex actions, like interpreting any text that might be entered into a chatbot, and reduces it to the most simple components. When it comes to AI interactions, you can imagine a spreadsheet with a bunch of keywords and keyword combinations that the AI is trying to match up to what it was told.

If it can find the right combination of words, in a given context, it will output a specific response. If it cannot identify the right words, it will need to take another approach or, in certain cases, may escalate the matter to a human. 

There are different chatbot examples for business when it comes to this approach. When someone interacts with the chatbot for a business, what they ask is fairly limited in scope. If the bot is connected to customer account information, it may have an easier time identifying a customer’s needs as well. 

A bot can also be used to collect key pieces of information. For example, Avis has used AI-powered virtual agents to save a minimum of 30 seconds on every call. Over the course of a day, this can free up a lot of time for each human agent. 

Finding the Right Pattern

All humans have an innate pattern-finding ability. This is why many electrical sockets seem to have faces and we can see shapes in the clouds. It’s also what has allowed people to easily identify edible plants among other types of dense foliage and other objects needed for survival.

Artificial intelligence AI has never had to grow and learn in this way, though it can be forced to. It’s possible to train AI agents to deep learning certain patterns. This is how some AI is able to solve certain types of captcha. The AI is trained by being made to view thousands of items or examples of what they need to recognize. 

In customer service, the AI system can recognize certain patterns and phrases within thousands of conversations between human agents and customers. It can then apply these in like situations. Hubspot has trained their chatbots to qualify leads through Facebook Messenger. These leads are then handed off to a human sales agent and they have a 40% higher engagement rating. 

This is also how chatbots are taught manners and “personality”. Instead of programming individual rules, which can be done but is often ineffective, the bot is given thousands of live examples of how everyday situations are handled by trained, human customer service agents.


How can a computer program learn to detect and process human emotions? This is the single hardest thing for an AI to learn to do, at present. The best method so far is a combination of the above- finding a pattern and seeing how other humans have responded to similar cues in language or vocal patterns. 

Though the AI doesn’t understand what it’s doing, it is capable of mimicry and, in some cases, mirroring based on examples and experiences it has encountered in the recent past. However, most AI these days are incapable of coming up with more than one way to handle a specific situation. It can be re-trained entirely, but cannot learn something new on top of a skill it already has. 

Specialized Knowledge

AI is most effective when the subject matter it has to talk about is limited. For example, if a chatbot is taught all about how to install a faucet, but the customer starts asking questions about rabbits, the chatbot is unlikely to know how to respond. 

However, having a limited set of knowledge and tools, with the right input, will create a much more convincing output. This, again, is how many business-oriented AI chatbots work- they have limited knowledge and training, but because of this specialization, they are more effective at handling the needs of specific customers. 

If all of these customer interactions are chat-based, it’s possible for a customer service agent to oversee as many as 10 different chat conversations at once. Many will resolve on their own, but if the agent needs to step in, they will be able to do so while maintaining the flow of the interaction. 

How to Build a Better Chatbot

Today, AI isn’t sophisticated enough to handle every imaginable task on its own. What it can do is perform tasks in a competent and friendly manner. When you design or set up a chatbot, it’s important to do the following three things. 

  • Have a Clear Goal in Mind – While AI can be used for diverse applications, there should always be a clear main goal. Often, this is basic information gathering so that a human customer service agent will have everything they need to complete the exchange.
  • Give the Bot an Out – If a chatbot doesn’t understand something, it tends to loop within the behaviors it knows unless it has been told not to. For example, if a bot has received two negative responses, it needs to escalate things to a customer service rep. 
  • Compare the AI to a Human Sample – All chatbots should be trained on many real samples, generated by actual use cases. This will give them more flexibility than scripted training materials.

Frequently asked questions about How does AI learn human responses

How does AI learn human responses?

AI learns human responses through a process called machine learning. Machine learning algorithms analyze vast amounts of data, including human-generated text, conversations, and interactions, to identify patterns and learn from them. By training on these data sets, AI models can understand and generate human-like responses.

What is the role of data in teaching AI to learn human responses?

Data plays a crucial role in teaching AI to learn human responses. AI models are trained on large datasets that contain examples of human conversations, including text-based exchanges, social media interactions, or chat logs. The more diverse and representative the data, the better the AI model can learn to generate accurate and contextually appropriate human-like responses.

How does AI ensure contextually appropriate responses?

AI models use various techniques to ensure contextually appropriate responses:
Context window, Attention mechanisms, Training on diverse data and Fine-tuning and feedback.
This iterative process helps improve the model’s ability to generate more accurate and contextually appropriate responses over time.