Master classes: Intermediate to Advanced AI Practices for UX Professionals

This Learning Track covers two intermediate master classes on AI practices for UX professionals.

• Master class 1: Increase efficiency in UX research with AI tools gets you up to speed with UX research with AI tools. It covers prompting techniques,  conducting interviews with AI bots, using AI assistance in analysing user interviews, and recognising the potential of AI for UX work.
• Master class 2: Advanced Prompting & Assistant Creation for UX Professionals builds on the first master class. It specifically addresses creating AI assistants for UX professionals.

This course is specifically targeting UX professionals and requires a solid understanding of user experience.

• Prompting techniques for UX professionals
• interview analysis with AI tools
• Hands on assistent development

On completion of this course, participants will be able to:   

• try out various AI tools in the research process and learn about their strengths and weaknesses
• be able to better assess the quality and reliability of AI-generated results
• know how to get the best results from AI tools through targeted prompting
• develop your own ideas on how artificial intelligence can support you in your daily work.
• Identify time-consuming, repetitive activities that can be supported by AI
• Evaluate which AI tools are GDPR-compliant and legally safe to use
• Apply prompt techniques to generate structured and meaningful analysis results
• Apply the 4-stage maturity model for systematic assistant development  
• Create knowledge-based assistants by structurally integrating domain knowledge  
• Design professional output formats and templates for consistent results  
• Develop systematic testing strategies to find and fix weaknesses  
• Apply advanced in-prompt engineering techniques for reliable responses  
• Evaluate when automation makes sense and plan integration into workflows  
• Build robust, production-ready AI assistants that handle edge cases elegantly  
• Transfer learned principles to their own use cases and challenges  
• Make the assistant's reasoning process transparent for stakeholders  
• Balance robustness and flexibility in assistant design 

Interactive workshops with exercises