When AI-Based Agents Are Proactive: Implications for Competence and System Satisfaction in Human-AI Collaboration
Abstract
As the capabilities of artificial intelligence (AI) technologies continue to improve, collaboration with AI-based agents enables users to be more efficient and productive. Not only has the quality of AI-based agents' outcomes increased, but they can now help proactively, and even take over entire work tasks. However, users need to be satisfied with the system to remain motivated to collaborate and engage with AI-based agents. Drawing on self-determination theory, a vignette-based online experiment was conducted that revealed that proactive (vs. reactive) help from AI-based agents leads to a higher loss of users' competence-based self-esteem and thus reduces users' system satisfaction. This effect is moderated by the users' knowledge of AI. Higher (vs. lower) levels of AI knowledge cause a greater loss of competence-based self-esteem through proactive (vs. reactive) help. The findings contribute to a better understanding of help from AI-based agents and provide important implications for managers and designers who seek to enhance human--AI collaboration.
Study specs
Vignette-based online experiment using self-determination theory as the framework to evaluate user responses to proactive vs. reactive AI assistance.
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Impact of proactive vs. reactive AI help on users' competence-based self-esteem and system satisfaction, moderated by users' AI knowledge levels.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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