# Assistant or Actor? Student Trust, Control, and Delegation Regret When Using a General-Purpose AI Agent

arXiv: https://arxiv.org/abs/2607.18257 · HTML: https://mlsanity.com/paper/2607.18257/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 14 May 2026.

## Builds on (most cited first, 14 of 14)

- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems](https://mlsanity.com/paper/2008.09100/) (2020)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [Towards Automating Data Access Permissions in AI Agents](https://mlsanity.com/paper/2511.17959/) (2025)
- [Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives](https://mlsanity.com/paper/2306.03289/) (2023)
- [Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data](https://mlsanity.com/paper/2508.06484/) (2025)
- [Belief Updating and Delegation in Multi-Task Human-AI Interaction: Evidence from Controlled Simulations](https://mlsanity.com/paper/2602.01986/) (2026)

## Built on by (most cited first, 1 of 1)

- [DelegationBench: Measuring When AI Agents Should Ask Before Acting](https://mlsanity.com/paper/2610.05532/) (2026)

## Abstract (arXiv)

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-text reflections analyzed through thematic coding. Three findings emerged. First, participants calibrated trust per task rather than per agent: they granted wide autonomy for advisory and low-stakes tasks but demanded confirmation for irreversible, externally visible actions. Second, irreversibility combined with external visibility, rather than stakes alone, appeared to drive trust withdrawal: the moderate-stakes email task triggered the sharpest drop in trust (M = 3.10) and the highest demand for approval (M = 4.65), whereas a high-stakes but verifiable task did not produce the same response. Third, delegation regret appeared consistently when the agent executed actions without preview, even when the output was rated as successful. We discuss implications for agent designs that expose action boundaries, support per-task autonomy policies, and separate advisory output from agentic execution.