对比新手与专家偏好，发现被动个性化有局限，需结合主动控制提升考试辅助效果。
Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking
- 通过被动个性化适配用户经验差异，减少任务负担。
- 实验显示助手感知度提升，但特定任务表现受限。
- 适合企业AI助手设计者，强调主动权的重要性。
新手与专家在任务导向对话中存在系统性偏好差异。然而，针对这些差异进行个性化是否真能提升用户体验与任务表现仍缺乏研究。为探究基于经验的个性化影响，我们构建了一个具备被动个性化功能的企业级AI助手，并开展用户实验，让参与者在限时考试中使用两个版本的助手完成任务。初步结果表明，被动个性化有助于降低任务负荷并改善助手感知，但暴露出特定任务中的局限性，可通过赋予用户更多自主权来解决。研究强调，在企业任务导向场景中，应结合主动与被动个性化以优化用户体验与效率。
原文摘要 · Abstract (English)
Novice and expert users have different systematic preferences in task-oriented dialogues. However, whether catering to these preferences actually improves user experience and task performance remains understudied. To investigate the effects of expertise-based personalization, we first built a version of an enterprise AI assistant with passive personalization. We then conducted a user study where participants completed timed exams, aided by the two versions of the AI assistant. Preliminary results indicate that passive personalization helps reduce task load and improve assistant perception, but reveal task-specific limitations that can be addressed through providing more user agency. These findings underscore the importance of combining active and passive personalization to optimize user experience and effectiveness in enterprise task-oriented environments.
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