That’s How AI-powered Chat-based Training Works with eggheads

That’s How AI-powered Chat-based Training Works with eggheads

Read the summary of our webinar to find out how you can improve operational learning from development to evaluation with eggheads and AI support. You will see practical applications in areas such as compliance, sales and customer service. You will also learn how to use chat-based microlearning with AI support.

The most important things in a nutshell

  1. Practical examples for the use of eggheads: Various application examples are presented on how companies use the microlearning solution to be more effective in areas such as compliance, sales and customer service. This includes updating processes, introducing new products, preparing for customer enquiries and communicating safety topics.
  2. Chat-based learning modules for effective learning: The use of short, chat-based learning modules will be emphasised to help convey key messages and raise awareness of important topics. This methodology is called Conversational Microlearning (More: What is Conversational Microlearning).
  3. Four main applications of Conversational Microlearning: Four main applications for this method are described:
    1. Just-in-time training for quick knowledge updates
    2. Performance support for direct application of knowledge
    3. Refresher training and
    4. Soft skills training.
  4. Creating and personalising learning content with AI: It explains how existing content can be used to quickly create initial drafts for chat tutorials or quizzes with AI support.
  5. Personalised feedback through simulations: The webinar highlights how the tool enables personalised feedback by allowing users to formulate their own responses in simulated scenarios, such as customer conversations or difficult situations. This allows users to practise their responses in realistic contexts and receive direct feedback on their answers.
  6. Analysing data to improve learning content: An important aspect is eggheads’ ability to collect and analyse detailed data from user interactions. This data helps to understand the needs and responses of the target group and to adapt and improve the training and content accordingly.

The entire recording (in German)

The slides in German (download here).

The summarised transcript

Welcome to today’s webinar. I’m glad you could join us. We’re going to focus on how you can improve operational learning with eggheads. You will learn how eggheads’ inbuilt AI helps you from developing new trainings to evaluating them.

In a previous webinar, we discussed the basics (watch the recording). Today it’s about what you can do specifically. We’ll look at real-world examples that show how organisations are using eggheads successfully.

Here are some examples:

  1. A company renews or reminds employees of its processes and checks whether they know them.
  2. A company launches new products and wants the sales team to be able to explain them well to customers.
  3. In customer service, a team prepares for increased enquiries on a specific topic. The topic is briefly refreshed.
  4. A company identifies a security issue and quickly provides information about it via a chat. This is not just about imparting knowledge, but also about raising awareness of the topic. There can be several chats over a period of time to keep the topic present.
  5. In our blog, you can find an example of chat-based microlearning from compliance in financial technology (fintech). It describes how a company carries out more than just the annual mandatory training. It’s about not just ticking off important information, but understanding and implementing it. One approach used by this company is the use of short, chat-based learning snacks. These help to embed key messages and ensure that the rules are present at all times, especially when they could potentially be breached.
  6. Another company uses similar methods to train teams that interact directly with customers, such as customer service or sales. These teams are empowered to have competent conversations.
  7. Another example is a company that is repositioning itself and introducing new brand values. Instead of limiting itself to a one-off event, it uses refresher training to keep the content alive.

These real-life examples show how conversational microlearning can be used. The aim is to explain and deepen topics. You can use so-called explanation bots or tutorial bots to repeat and consolidate knowledge, check the teams’ understanding and simulate certain situations.

There are four main applications for this method:

  1. Just-in-time training for quick, agile knowledge updates.
  2. Performance support to apply practical knowledge directly.
  3. Refresher training to ensure that what has been learnt is internalised and applied.
  4. Soft skills training to improve the interpersonal skills of the teams.

In our work, we have observed that chat-based learning methods work particularly well in areas such as sales, customer service, compliance, IT, security and HR. For example, they are suitable for employee appraisals or to address topics such as diversity and inclusion.

These learning methods look like normal chats that we would have with a work colleague or friend. You can make them accessible via a web link or our Microsoft Teams app. By using Microsoft Teams, you have everything in one place – notifications and the learning experience – where people are already working together (more on the egghead Microsoft Teams app).

What can you do now?

Automated first drafts

With the support of AI, you can use your existing content and create a first draft for a chat tutorial or chat quiz. This makes the process quicker and easier than if you had to start from scratch.

You can add images or GIFs to the drafts, refine them further and then check whether everything is correct. You retain control over the content. This approach combines the advantages of AI with those of rule-based chatbots and gives you more control over the learning experience you offer your colleagues. By combining rule-based and AI-powered chatbots, you can create hybrid learning bots.

Once you’re done, you can share the chat via your existing channels or our Microsoft Teams app.

Personalised feedback

Another aspect is personalised feedback. You can practise how to respond to such situations, e.g. angry customers, in a simulation with your customer service team. Participants then receive feedback on whether their responses are in line with company guidelines and where there is room for improvement.

An analogy from the language learning area of Duolingo: You are guests in a Parisian bistro. The waitress takes your order; you answer and receive feedback from Duolingo on how you can improve your communication.

You can now use this experience with your own content.

There are other good examples of the use of chat-based simulations in sales, such as dealing with objections. In compliance, for example, you can train how to react in difficult situations. These techniques are also useful for leadership training to conduct or prepare for difficult conversations.

AI insights

Another important point is learning from data. With our tool, you can analyse detailed data such as click numbers and responses. This helps you to understand the needs of your target group, evaluate the effectiveness of training and find opportunities for improvement.

In a previous example, we collected 95 different free-form answers. With AI, you can now analyse the open answers of your participants to identify patterns and common themes. This helps you to quickly gain an overview and identify potential for improvement.

Automated translations

Another important aspect is translation. Let’s assume we have created a chat in German about objection handling and want to offer it in Italian. Our AI can translate everything we have prepared – media, jumps, texts – directly. It is advisable to have the translation checked by a native speaker, but it saves you a lot of time compared to manual translation.

In summary, you can use four AI-supported functions that support you throughout the entire training process: creating content, personalising the learning experience, analysing data and translating into different languages.

These were the functions I wanted to show you. Now I’ll finish the presentation and answer any questions you may have.

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