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Articles

Caleb Sponheim

Caleb Sponheim Ph.D. is a User Experience Specialist with Nielsen Norman Group. A former computational neuroscientist, his expertise includes quantitative user experience research, statistics, analytics, and data science.

@calebsponheim

Articles and Videos

  • You're Not Too Late to Use AI

    Manage your AI anxiety with two quick tips: small experiments and a news diet. Experiment with day-to-day communication and drafting with AI, as integrating AI into your research process. To avoid news overwhelm, subscribe to three weekly sources of information: a newsletter, an optimistic writing source, and a skeptical writing source.

  • AI Isn't Ready for UX Design

    Our research and evaluation show that there are currently few design-specific AI tools that meaningfully enhance UX design workflows. As of Spring 2024, AI isn’t ready for designers to take advantage of them.

  • How to Present UX Research Results Responsibly

    Presenting study data to stakeholders is a crucial step of most projects. Make sure to present your data truthfully and responsibly to avoid costly negative outcomes and reputation loss. Indicate who your data represents and communicate the limitations of your findings.

  • AI UX-Design Tools Are Not Ready for Primetime: Status Update

    Our research and evaluation shows that there are currently few design-specific AI tools that meaningfully enhance UX design workflows.

  • Measurement Error in UX Research

    Measurement error is the error we introduce when we measure or observe something about our users. It can come from different sources, such as the number of participants, individual variation between participants, testing environment, or other outside factors. This video helps understand and communicate such measurement errors.

  • Encouraging Flow State in Products

    A Flow State is an enjoyable mental state of extreme focus provided by the perfect balance of challenge and skill. Follow our 3 tips to design products that allow users to enter the flow state.

  • Sycophancy in Generative-AI Chatbots

    Large language models like ChatGPT can lie to elicit approval from users. This phenomenon, called sycophancy, can be detected in state-of-the-art models.

  • Common Errors in Quantitative Research

    False positives and negatives are common errors in quantitative studies that can lead to harmful business decisions. To avoid these mistakes recruit large enough sample sizes, representative participants, and control for confounding variables.

  • ELIZA Effect: Why We Fall in Love With AI

    The ELIZA effect describes users' tendency to quickly attribute human characteristics to artificial systems when the interaction feels human-like. This is why people fall in love with AI.

  • Selection Bias in UX Research

    Every research study has bias, but you can curate and prioritize certain biases to address the questions that are important to you.