Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
A workshop for creating mental models of user expectations for AI products. It includes exercises for analyzing user groups, their goals, and current processes, as well as creating onboarding messages and testing user comprehension. The goal is to help teams develop effective AI products by understanding user mental models.
What you will learn
Typology: Exercises
1 / 13
This page cannot be seen from the preview
Don't miss anything!
Example product: AI that automatically prioritizes new emails and sorts inbox according to their priority Key questions
User group A Example: employee at large company using email for work User group B Example: Everyday consumer using a free email service
User group A Example: Process - Frequently check email, individually triage each message. Uniformity - Highly variable. User group B Example: Process - Scan inbox for important mail, ignoring the rest Uniformity - Highly variable.
User group A Example: Process - Set up multiple custom filters, notifications, and labels and folders Uniformity - Highly variable. User group B Example: Process - Set up filters, systematically unsubscribe from lists to free up inbox. Uniformity - Highly variable.
User group A Example: Priority of email varies by: ● Number of recipients (just user or large group) ● Frequency of sending emails to contact ● Speed at which user opens and replies to email User group B Example: Priority of email varies by: ● Contact's membership in a specific group ● Active orders or subscriptions ● Length of communication
User group A Example: Making the system seem “human” might imply that the AI actually does have the same knowledge and context as the user, which conflicts with the key cause and effect relationships the user needs to understand. User group B
All users Example: AI works as a binary yes / no categorizer for censoring content in an online forum, but users expect gradations of control.
Version 1 Version 2 Version 3
Research protocol questions ● First, show users your initial onboarding concepts, then ask them questions like: ○ Explain in your own words what [product] is. ○ Explain in your own words how [product] works. ○ Based on what you saw, describe what using [product] will be like. ○ Based on what you saw, how useful do you expect [product] to be for you? ○ Any additional expectations you have about [product] based on what you read? ● Next, if you have any wireframes or demos or working prototypes of your product or feature, show it to the user after walking through your onboarding experience concepts. ● Lastly, after interacting with both the design concepts and the AI prototype, have users describe how the AI experience compared to their expectations.
