Assistant or Actor? Student Trust, Control, and Delegation Regret When Using a General-Purpose AI Agent
When AI agents shift from answering questions to taking actions, users face a new problem: deciding what to delegate, to a system whose action space they cannot fully anticipate. We call the resulting dissatisfaction delegation regret, a pattern in which users regret not that the agent erred, but that it acted beyond what they would have authorized. In a controlled study, 20 university students completed five common daily tasks using OpenClaw, a general-purpose AI agent, across tasks chosen to vary in privacy, stakes, and reversibility. For each task we measured trust, perceived control, transparency, supervision burden, and approval preference on 5-point Likert scales, and collected free...
| Authors | Shiva Pochampally, Shengwei An, Yan Chen |
|---|---|
| Primary category | cs.HC |
| Published | 14 May 2026 |
| Last updated at source | 2 October 2026 |
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