Machine Learning UX Exercises
Explore hands-on “Machine Learning UX” exercises to sharpen your skills and level up your craft. Want more? Browse all search results
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Labeling system
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AI writing models
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Microcopy should be live text
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Adding a professional touch
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Mind the choice and order of words
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Make the Unsubscribe button easy to locate
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Selecting the right tool
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Keep user experience at the forefront
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Reference artists
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The importance of iterations
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Don’t rely on AI tools solely
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Luminance type
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UX designer's evolving role with AI
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Limitations of traditional methods
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Unstable performance and usability issues
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Selecting nested layers
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Malone’s theory of intrinsically motivating instruction
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Automation vs. AI-powered experiences
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Train personnel to use new tools
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Personalization at scale through AI
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Learn from the community
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Lepper’s instructional design principles for intrinsic motivation
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Defining AI in UX context
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Predictive interfaces and anticipatory design
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Collaborating with data scientists
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Data-driven vs. rule-based decision making
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Include both intrinsic and extrinsic motivators
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Visual or wording tricks
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Setting realistic AI expectations
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Establishing ethical guardrails
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Poor onboarding and early churn
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Data bias: origins and impacts
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Articulating model constraints
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Characteristics of AI-powered experiences
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Potential abuse or manipulation
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Value alignment in AI design
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Research techniques for uncovering mental models
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Augmentation over automation
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Selecting layers
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Mapping AI tools to product tasks
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Inclusive AI design principles
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Overfitting: when models learn too well
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Recognition vs. reasoning abilities
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Signs a problem needs AI solutions
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Meaningful human agency
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Designing for graceful AI failure
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Generative AI capabilities and limitations
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Designing adaptive user interfaces
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Unpredictability and edge cases
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