AWS Machine Learning Blog

Building an omnichannel Q&A chatbot with Amazon Connect, Amazon Lex, Amazon Kendra, and the open-source QnABot project

For many students, embarking on a higher education journey is an exciting time filled with new experiences. However, like anything new, it also can also bring plenty of questions to answer and obstacles to overcome. Oklahoma State University, Oklahoma City (OSU-OKC) recognized this, and was intent on providing a better solution to address student questions using machine learning (ML) technology from AWS.

They knew that if they could develop a solution that accurately anticipated their students’ needs and delivered timely and relevant information, they could boost their chances of attracting future students. After all, universities need students the same way businesses need customers.

“The first thing we wanted to address was the lack of visibility we had into customer sentiment at any given time,” says Michael Widell, Interim President at OKC-OSU. “Building on that, we also had a real focus on consistency and accuracy of information—it mattered to us that current and future students could rely on the information they were getting across school and faculty communication channels.”

The team identified conversational chatbots as a way to address the information gap that students face. ML-powered chatbots are dynamic, and help connect with students through the communication channels they prefer, whether that’s a website, phone, chatbot, or by asking an Alexa-enabled device.

With this in mind, OSU-OKC began working with AWS Professional Services in January 2020, and became the first university to deploy a call center using Amazon Connect and the QnABot.

Amazon Connect is a cloud contact center that provides a seamless experience across voice and chat for customers and agents. The QnABot is an open-source project that uses Amazon Lex to provide a conversational interface for your questions and answers, and can be applied to a host of communication channels, including websites, contact centers, chatbots, collaboration tools like Slack, and Amazon Alexa-enabled devices.

Deploying QnABot in the call center

Although OSU-OKC’s use of the QnABot evolved throughout 2020, its initial area of focus centered on boosting call center efficiency. They achieved this by automating answers to student FAQs, thereby delivering accurate and up-to-date information, reducing call hold times, and enabling human call center agents to focus on handling higher-value interactions.

The following diagram illustrates the solution architecture.

The following diagram illustrates the solution architecture.

For OSU-OKC, QnABot simplified bot deployment and administration, allowing even non-technical users to maximize the impact of the solution by allowing them to:

Extending the QnABot to the website

After implementing the QnABot to assist agents inside their call center, OSU-OKC decided to extend the bot’s reach to the university’s website. They used the AWS open-source Amazon Lex Web UI project, a sample Amazon Lex Web UI that helps provide a full-featured web client for Amazon Lex chatbots.

After content was gathered from the campus, creating question and answer responses for the bot was an easy process. The content designer provided customization options that allowed for organization and readability. The built-in test features aided the tuning and development process by attributing a matching score to a response.

Shortly after expanding the QnABot to their website, OSU-OKC realized that providing more channels for students to interact with didn’t dilute engagement levels. In fact, they increased overall engagement from their student body and doubled the average number of conversations with students.

Adding the QnABot to the university website wasn’t a replacement for human interaction; it was an aid to increase quality interactions by reducing repetitive phone traffic. Try asking OSU-OKC bot, OKC Pete, some questions of your own via the university website.

Try asking OSU-OKC bot, OKC Pete, some questions of your own via the university website.

OKC Pete on the university website

Equipping the QnABot with more responses

While QnABot answered high volumes of questions for students and delivered consistent service at scale, the OSU-OKC team learned a great deal about student sentiment by observing which questions the QnABot couldn’t answer.

For example, some questions highlighted how much prospective students knew about the campus and its resources. Incoming students asked about dorms when in fact the campus doesn’t have any student housing.

The team could use the QnABot’s Content Designer UI to continuously enhance the bot, and equip it with appropriate responses about student housing or any other campus resources. This helped students avoid a phone call, which freed call center agents to focus on more critical or higher-quality interactions.

This flexibility proved particularly helpful during the onset of the COVID-19 pandemic in the spring of 2020. OSU-OKC was able to rapidly expand the newly deployed QnABot’s knowledge base to include answers to many pandemic-related questions. Students and parents could quickly get answers to the questions that mattered to them via the QnABot-assisted university call center or via the website chatbot.

Scaling QnABot’s knowledge with Amazon Kendra

QnABot’s Content Designer UI allowed OSU-OKC to add new questions and answers to the bot when they identified a gap. However, the team also wanted to ensure that customers could still get answers when a question had not yet been added.

To achieve this, they used Amazon Kendra, a highly accurate intelligent search service. In the summer of 2020, the team at OSU-OKC integrated the QnABot with Amazon Kendra to enhance the accuracy and relevance of responses in the following ways:

  • Use the document index in Amazon Kendra as an additional source of answers when a question and answer isn’t found in QnABot’s knowledge base. This allows QnABot to find answers to questions that may not have been added to its knowledge base, including unstructured data contained in word documents or PDFs that have been indexed by Amazon Kendra.
  • Without extensive QnABot tuning, the natural language processing and reading comprehension capabilities of Amazon Kendra more accurately understand user queries, and its ML models expertly handle variations in how users phrase their questions to increase search accuracy and return relevant responses to user queries.

By using ML to automate the handling of common customer questions via their call center and website, OSU-OKC ensured consistent service levels even during their busiest time of year. Widell says, “During peak we can receive over 2,000 calls, which is too many for one agent to handle—however, since launching the QnABot, it’s supported over 34,000 conversations and saved 833 hours in staff time, while ensuring every customer received the same level of service and accuracy.”

Creating your own QnABot and integrating with Amazon Kendra

To get started on your own QnABot journey, see Create a Question and Answer Bot with Amazon Lex and Amazon Alexa. The section Turbocharging QnABot with Amazon Kendra outlines how to integrate QnABot with Amazon Kendra. If you want to follow OSU-OKC’s lead and add the QnABot to your website, you can take advantage of our companion Chatbot UI project.

As you think about configuration and deployment, consider the following options:

  • Deploy the QnABot and Chatbot UI yourself (self-serve), using the project as is
  • Make your own customizations and enhancements to the open-source code
  • Follow OSU-OKC’s example and contact AWS Professional Services for expert help to customize and enhance QnABot, and to integrate with your own communication channels

For more information, watch the team at OSU-OKC present their QnABot solution at Re:Invent 2020.

Conclusion

The team at OSU-OKC is excited to build on the early success they have seen from deploying the QnABot, Amazon Kendra, and Amazon Lex. “For customers and students, this has been the most impactful technology that we’ve implemented,” Widell says.

Our overarching vision for ML technology will evolve student interactions from being transactional exchanges to becoming more meaningful experiences, allowing us to easily connect to customers, understand their needs, and serve them better. Widell adds, “In the future, we hope to expand our use of QnABot to provide personalized information to students as it relates to their academic schedules, advisement, and other relevant information related to their course of study.”

About the Authors

Bob StrahanBob Strahan is a Principal Solutions Architect in the AWS Language AI Services team.

 

 

 

 

Michael Widell is the Interim President at OSU-OKC. As an innovative agent of change he has worked to strengthen organizations through redesign and resource optimization, allowing individuals to excel, and deliver transformative products and services. In his career, Widell has also held leadership positions in the private sector for AT&T and key roles within the General Office of Walmart Inc. where he began his post collegiate career.