arXiv:2606.30863v1 Announce Type: new Abstract: Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified. We argue this assumption is unrealistic. Users often lack the domain knowledge to have completely specified preferences; if asked about their preference on some feature, the user may be unable to answer without the agent helping the user to learn some domain knowledge needed to form a preference for that feature, e.g., via examples or explanations. To formalize these principles
The increasing sophistication and widespread deployment of AI agents make the current paradigm of 'expert user' assumption increasingly untenable as agents take on more complex tasks.
A strategic reader should care because this development fundamentally changes the human-AI interaction paradigm, moving from mere task execution to collaborative preference formation and knowledge acquisition, which could accelerate AI adoption and utility.
AI agents will no longer simply elicit predefined preferences but actively help users construct and refine them, blurring the lines between user interface, expert system, and learning assistant.
- · AI platform developers
- · Customer service sectors
- · Education and training companies
- · SaaS providers
- · Simple chatbot providers
- · Businesses relying on explicit user configuration
- · Purely transactional AI models
AI agents become more adaptive and capable of handling poorly defined tasks, improving user satisfaction and expanding their utility.
This leads to AI systems becoming de facto knowledge transfer mechanisms, accelerating domain expertise acquisition for users across various fields.
The enhanced user-AI collaborative learning model could fundamentally alter professional training, skill development, and expert knowledge dissemination, potentially creating new forms of digital apprenticeship.
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Read at arXiv cs.AI