Is Passive Expertise-Based Personalization Enough? A Case Study in AI-Assisted Test-Taking
This work addresses the problem of optimizing AI assistants for enterprise task-oriented environments, but it is incremental as it builds on existing personalization concepts.
The study investigated whether passive expertise-based personalization improves user experience and task performance in AI-assisted test-taking, finding that it reduces task load and improves assistant perception but has task-specific limitations requiring more user agency.
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.