The Psychology Behind Conversational AI Design

When you think of a “chatbot,” what comes to mind? Most people tend to picture a small pop-up at the bottom right of a screen, prompting the user to respond with something like “What are your business hours?” or “Schedule an appointment.” The basic concept of a chatbot was first unearthed by Alan Turing, the founder of computer science, in 1950 – and Joseph Weizenbaum then created the first chatbot 16 years later in 1966.

Conversational AI Design

After Turing’s innovation, technology has since advanced chatbots to better derive insights and information from human input using Natural Language Processing and Understanding (NLP and NLU) combined with artificial intelligence to create the utmost human-like chat experience online. But of course, to replicate human interaction, we need to not only understand but also predict human psychology – and that’s where conversational design comes into play.

Simulating human dialogue is its own oxymoron in being simply complex. Before delving too deeply into the models behind entity extraction or intent generation, we need to understand the end user’s persona, identify the chatbot’s primary use case, and carefully design a conversational architecture that would be useful and keep the user engaged. Everything in the language that the chatbot uses can make or break an overall customer or employee experience. From apologizing in the chatbot when error handling to adding a seasonal greeting, or even just ending a conversation with a quick quip, word choice can heavily impact general user perception of the chatbot’s capabilities and the brand it represents.

Advanced Conversational Design

One of Aisera’s strengths is its robust Conversational AI. Misspells, shortened methods of the same user request, or even long, rambling sentences are all easily recognized. For example, “wait, can you show my profile,” “I don’t know what to do,” “who am I,” “can I see my department,” “show my profile,” would all return the same result of showing the end user’s profile. On top of genuinely understanding user intent behind requests, Aisera can easily simulate real-world, human-like conversations using Context Switching in Conversational AI, allowing the user to change context midway but go back to what he was doing earlier. This is something that traditional declarative chatbots (chatbots that simply return information via buttons about business hours, location, basic information) cannot do because they do not have context-awareness as Aisera does.

Even when a user’s request is unclear and matches multiple results – something like “address,” Aisera’s AI disambiguation model can return several different matches and allow the user to select and clarify the desired result. For instance, “I’d like to address my benefits” versus “I’d like to update my address.” Despite using the same keyword of “address,” Aisera’s AI disambiguation model can pick up on keyword contexts and returns the correct section of the KB article or relevant action for the user. Typical chatbots are unable to do either and distinguish the chatbot at a level closer to the human-like language we want to achieve.

Talking the Talk & Walking the Walk

Finally, Aisera’s easily customizable, yet out-of-the-box Conversation Messages, are readily available system messages that serve as informational statements and error handling specific messaging. Additionally, these act as guides for the user in all types of scenarios through various processes, such as providing a transition into resetting a password through Okta Verify or suggesting an article to read instead of going through with an action flow. Most importantly, they lay down boundaries for the user to clearly define what it can and cannot do, apologize for any temporary inconveniences, and provide a polite transition into its answers to the user’s requests.

A chatbot’s exception and error handling abilities should enable end-users to continue conversations even when the system is encountering an issue with another service or a workflow. When it comes to Aisera, customers and users are still able to get help or answers to their questions even when Aisera cannot find what they are looking for – whether it is suggesting submitting a ticket to a relevant department or connecting the user to a live agent, the chatbot interface can provide an intuitive option to exit a workflow or escalate to a live human agent at any time. In the end, conversational AI design is focused on recreating the human-to-human conversation, but online through a chatbot. Everything that goes into the user experience matters – the AI models, the sentiment and language used, even the visual components and how information is presented. When designing for conversational AI, aim to be as human-like as possible!

The Brains Behind the Brawn

So what does all this have to do with psychology, anyway? Many are familiar with the concept of the  “Uncanny Valley,” but for the uninitiated, this refers to the paradigm of how humans view human-like interactions with machines. It usually pertains to machines that look like humans, yet something about them is out of place. Something similar to the quirky animatronics in campy science fiction movies, humans, don’t know why they have an off-putting feeling, but they do have a feeling. Now we can apply this same concept to designing conversations with a chatbot. The conversational interface must walk the fine line between sounding human enough to encourage natural-language interactions without leaving the human user feeling like they have been tricked into conversing with a computer program.

While it might not seem like a big deal to some people, designing these conversational flows presents a unique challenge when looking at AI chatbots that learn from each engagement. As the AI adapts to user requests, evolving to include language that resonates with specific personas, the chatbot itself may even begin to sound a little too human for comfort. This is where AI and linguistic experts come into play, building out complex and comprehensive ontologies and taxonomies for the bot to draw from and add to it autonomously. Just like how a question can be phrased a million different ways, chatbots can answer it with any number of permutations and combinations!

The future of conversational chatbot experiences is all about creating trust between the user and chatbot by delivering relevant resolutions with each and every interaction. Enterprises are augmenting their customer support and employee service desks with Conversational AI to provide an AI-powered around-the-clock point of contact whenever a user needs it. Having a trustworthy chatbot available 24/7 is important to create a psychological sense of safety for users; they know that help is always there if and when they need it. For any business, this should be a high priority when it comes to planning out a company roadmap.

Aisera offers the most feature-comprehensive self-service automation solution on the market, which blends Conversational AI and Conversational RPA into one SaaS cloud for IT Service Desks and Customer Services. Leveraging these advanced capabilities, Aisera proactively auto-resolves requests to meet ever-growing user demands for quick responses, immediate resolution, and personalized interactions across all channels. With this scalable, easy-to-deploy solution, your organization can improve service team productivity, reduce operating costs, and enhance user and employee experiences.