Speaking the Right Language: The Impact of Expertise Alignment in User-AI Interactions

2025-02-25

AI Summary

This study investigates how the alignment between AI response linguistic sophistication and user domain expertise affects user experience, with task complexity as a moderating factor. Drawing on 25,000 real-world Bing Copilot conversation logs, we employ automated domain expertise annotation, multi-dimensional experience metrics (satisfaction, engagement), and causal inference techniques. Our key contribution is the first empirical identification of “expertise alignment” as a core dimension of human-AI interaction quality: expert-level responses—constituting 77% of the corpus—consistently improve satisfaction, whereas responses below the user’s expertise level significantly degrade experience, especially for complex tasks. Moving beyond conventional language- or task-based matching paradigms, we formalize the “expertise alignment” principle, offering actionable, empirically grounded design guidelines for dynamically calibrating AI response granularity to user expertise.

Technology Category

• Humans and AI: User Experience and Usability
• Natural Language Processing: Question Answering
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Using a sample of 25,000 Bing Copilot conversations, we study how the agent responds to users of varying levels of domain expertise and the resulting impact on user experience along multiple dimensions. Our findings show that across a variety of topical domains, the agent largely responds at proficient or expert levels of expertise (77% of conversations) which correlates with positive user experience regardless of the user's level of expertise. Misalignment, such that the agent responds at a level of expertise below that of the user, has a negative impact on overall user experience, with the impact more profound for more complex tasks. We also show that users engage more, as measured by the number of words in the conversation, when the agent responds at a level of expertise commensurate with that of the user. Our findings underscore the importance of alignment between user and AI when designing human-centered AI systems, to ensure satisfactory and productive interactions.

Problem

• Impact of expertise alignment in user-AI interactions
• Negative effect of agent expertise misalignment on user experience
• Importance of matching AI expertise level to user expertise

Innovation

• Expertise level alignment
• User engagement metrics
• Human-centered AI design