Just a few years ago, many companies were producing context-sensitive help because they realized that the user needs to get only a small portion of information relevant at this particular moment. But then the user context mainly meant the current user location within the UI. Help topics were associated with windows so the topic related to the currently opened window was displayed when the user hit F1.
Today, many additional factors determine the context. This includes user goals, skills and experience, job role and responsibilities, geographical location, history of previous interactions, tasks that the user is doing right now, and so on.
Different combinations of the elements of the context determine the elements of the content that should be given to the user.
Some information about the context can be captured automatically. For example, information about the user’s location and at least some basic profile data can be accessed quite easily. However, information about the current goals and specifics needs to be unveiled through a conversation with the user.
This is one of the tasks that chatbots can do. They gather information about the user’s context by asking them questions, and then based on their answers, they can offer a specific advice.
The chatbot needs a knowledge base in which information that may address the user’s question is stored. DITA A.I. provides a chatbot engine that understands user’s questions asked in a natural language and matches them to the information stored in the central content repository.