Please help me to compare two new ways of learning with AI notebooks on the topics of Ai and UX design Best practices…
Test the two new notebooks I’ve created and curated with identical content sources about AI and UX design Best practices and considerations. They both contain the same content.
It would be great when you could share your findings about your learning experience from each, in some detail (take your time and come back to it…).
That will help me very much to refine and develop this concept of aiPods for specific user needs and audiences.
Topic: Ai and UX design Best practices with grounded theory, practical design assignments and How-tos, and useful design tasks.
Audience: Professional UX designers and students (interested in designing AI for humans first)
Contact me here to get your private url link for option 2. (Google NotebookLM, Interactive mode). It is free.
1. Cobundle
Title: aiPod 013 – AI UX design How?
(free access)
AI uses my curated documents sources only. No Audio podcast (No Audio Interactive mode)
The provided materials focus on user-centered AI design, emphasizing the creation of ethical and effective AI systems. A central theme involves balancing AI UX design automation and augmentation.
2. NotebookLM
Title: aiPod 013 – Interactive AI Workshop: Designing Effective AI Interactions: Use Cases and UX design Considerations
(free access Google account, includes Interactive mode, Audio Overview)
AI uses specific curated document sources only.
This type of AI NotebookLM includes an engaging multi-modal user Audio voice interaction mode with podcast hosts
Just Play the Audio Overview to the left pane, allow your microphone and click on: ‘Interactive mode, BETA‘) to ask your questions in context to AI- sourced personas, Aiko & Blaise)
The provided materials focus on user-centered AI design, emphasizing the creation of ethical and effective AI systems. A central theme involves balancing automation and augmentation to foster a healthy user-AI relationship, where AI enhances human capabilities without sacrificing control. Key considerations include evaluating AI suitability, ensuring user control and engagement, and maintaining AI transparency through explainable decision-making. Furthermore, these resources examine precision versus recall, bias mitigation, variability in AI outputs, and the importance of user feedback in refining imperfect AI. The resources emphasize the importance of testing AI data alignment to the user and using mental models to align AI behavior with user expectations, ultimately aiming to create AI solutions that are both beneficial and trustworthy.
Contact me here to receive my free link to this NotebookLM and test your questions to Aiko & Blaise, with the ‘Interactive mode, (BETA)‘.
