Build course overview
What are main components of the course, name, description, and how long will it be?
Define learning objectives
What should the participants be able to do by the end of the course?
Identify key content and tools
What content, videos, and resources will reinforce the course?
Identify KANU tasks and badges
What KANU features and concepts will be essential for the level?
Laying out the levels
Build the level outline
How will the course be organized?
Identify the modules for each level
What are the reading, task, and/or quiz modules?
Using ChatGPT for course buildout
Define the objective
Who is the level for, what is the level about, why is the level being created?
Add context
How the level will be constructed with specific reading, tasks, quizzes, and badges?
Prepare questions
Anything specific on your mind about the level or pending modules?
Once the outline is created, get KANU team review
Building the modules
After review, add the name, description, and outcomes of each module
Clear, action-oriented names that reflect the core activity or learning objective.
Develop the chat-bot interactions for each module
Develop a mix of messages, quizzes (if applicable), media, tasks, and practical applications
Use A/B TestQuest as an example, keeping in mind the applicable road mapping components
Using ChatGPT for content creation
Chatbot messages
Minimum: Aim for at least 3-5 messages to establish a basic interaction.
Optimal: Generally, 5-10 messages allow for a concise yet comprehensive explanation of a concept, as well as a brief interaction.
Maximum: More than 15 messages may overwhelm the user, unless the content requires in-depth discussion or the messages are very short and engaging.
Quiz Questions
Minimum: At least 1 question to test understanding of a very specific point.
Optimal: Around 3-5 questions provide a good balance to assess comprehension without causing fatigue.
Maximum: More than 5 questions should be considered if the module covers a wide range of material or if the quiz is intended as a major review point.
Considerations
Content Density: If the module is information-heavy, keep the number of messages concise and focused on key points to avoid cognitive overload.
User Engagement: Use the chatbot to ask probing questions that prompt the user to think and respond, which can be counted as part of the interaction.
Module Length: Shorter modules can be more engaging and should contain fewer messages and quiz questions to match the quick pace.
Quiz Complexity: The difficulty of quiz questions should also be considered. Fewer, more complex questions may be better than many simple ones.
Attention Span: Keep in mind the average user attention span in digital learning environments; interactions that take longer than 5-10 minutes may lose user engagement.
Conduct multiple reviews to ensure all messages and quizzes are conversational
Ask yourself, “Would I say this out loud to a user going through this module?”
Text-based decision tree for each module
Final deliverable reviewed by the KANU Team
Getting ready for development
Once the decision-tree content is approved, proceed with constructing the decision tree itself, using the prebuilt components
