Let’s not pretend we all completely understand the inner workings of artificial intelligence models. Neither do your users. Thankfully, you don’t need to fully understand them to use them. The same has always been true of a computer.
However, the better you understand some of the basics, the more accurate your mental model is. Accurate mental models make you more efficient and help you avoid problems.
These resources can strengthen your mental model of how AI functions and illuminate the mental models your users have for these systems, which dictate their usage of them.
How Artificial Intelligence Functions
Neural networks, unsupervised learning, hallucinations, and sycophancy? There is an entirely new vocabulary around large language models (LLMs) that suddenly gets thrown around like we all know what they are and why they matter. The resources below break these (and other) relevant AI concepts down into normal language.
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This is a collection of fundamental AI terms and definitions. |
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AI introduces a third UI paradigm called intent-based interaction, where users state goals rather than specific commands. |
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This is a non-technical explanation of how large-language models (LLMs) process user inputs and create their responses. |
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Large-language models (LLMs) learn language and develop their individual styles through multiple phases of training. |
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This article explains why AI tends to hallucinate, why that is hard to change, and how it should affect AI product design decisions. |
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Large-language models (LLMs) are built to prioritize pleasing users over validating the accuracy of their responses. |
Conversational and Prompt Authoring Patterns
Users' interactions with prompt-based AI tools follow clear, predictable patterns. These resources document and describe the partners we’ve observed in our research since the early days of ChatGPT.
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Users often include format or structure specifications in prompts to ensure that the chatbot’s response satisfies those requirements. |
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Users tend to follow common patterns and structural elements while prompting LLMs. |
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Research shows six types of AI conversations: search queries, funneling, exploring, chiseling, expanding, and pinpointing. |
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Accordion Editing and Apple Picking: Early Generative-AI User Behaviors |
article |
Two patterns describing the shifting and complex ways in which users carry on task-related conversations with genAI chatbots. |
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article |
AI-image-generation users often follow a similar creative process: ideate, generate, refine, and export. |
Trust and Anthropomorphism
People inherently seek interactions with people (or systems) that seem to listen to them and understand their needs. The better the AI is at seeming as though it understands a user, the more they trust it and project human-like attributes on it.
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People trust AI more when it seems smart rather than sentient. AI emotions can reduce trust in factual, task-oriented work. |
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Users quickly attribute human-like characteristics to artificial systems, which reflect their personality back to them. |
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When Should We Trust AI? Magic-8-Ball Thinking and AI Hallucinations |
article |
It’s easy to place too much trust in genAI tools. Use only information you can verify or recognize to be true. |
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Users tend to anthropomorphize AI at four different levels: courtesy, reinforcement, roleplay, and companionship. |
Information Seeking and Search Behaviors
LLMs have introduced a new way for users to seek answers to their questions — a role search engines have dominated for years. While AI has made certain aspects of information seeking easier, it is far from perfect. It remains difficult to know whether its answers are true and trustworthy.
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AI can help people who don’t know the words for what they are looking for, but many users still don’t use it effectively. |
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Generative AI is reshaping search, but long-standing habits persist. |
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The polish of AI outputs and the high interaction cost make it difficult to check for errors. |
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Information Foraging with Generative AI: A Study of 3 Chatbots |
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Users expect AI chatbots to aggregate information in a concise and specific manner, while fully considering contextual cues. |
Understanding and Adoption of AI UIs
While there has been an incredible adoption rate of AI across many different user groups, the learning curve can be steep. When users have inaccurate mental models of how the system functions or lack a larger vision of its potential, their usage of it can be underwhelming.
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Out of ~5,000 working Americans, 81% reported they did not use AI at work. |
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Can Users Control and Understand a UI Driven by Machine Learning? |
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Users struggle to understand how complex personalization systems turn their actions into recommendations. |
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article |
First-time users of Chinese gen-AI chatbots struggled to understand the tools’ functionality. |
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ChatGPT, Bard, or Bing Chat? Differences Among 3 Generative-AI Bots |
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Users preferred ChatGPT and Bard over Bing and shared opinions about all three. |
Podcast Episodes
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24. Artificial Intelligence: What Is It? What Is It Not? (ft. Susan Farrell) |
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Susan Farrell shares some early insights into the potential benefits and drawbacks of AI systems. |
