Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912736341917696 |
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| author | Siyan, Li Zhang, Jason Maharaj, Akash Shi, Yuanming Li, Yunyao |
| author_facet | Siyan, Li Zhang, Jason Maharaj, Akash Shi, Yuanming Li, Yunyao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_23376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking Siyan, Li Zhang, Jason Maharaj, Akash Shi, Yuanming Li, Yunyao Human-Computer Interaction Computation and Language 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. |
| title | Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2511.23376 |