# Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration

arXiv: https://arxiv.org/abs/2402.18498 · HTML: https://mlsanity.com/paper/2402.18498/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2024.

## Built on by (most cited first, 13 of 13)

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- [Making Sense of AI Limitations: How Individual Perceptions Shape Organizational Readiness for AI Adoption](https://mlsanity.com/paper/2502.15870/) (2025)
- [A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models](https://mlsanity.com/paper/2504.02793/) (2025)
- [Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation](https://mlsanity.com/paper/2602.12089/) (2026)
- [AI in the Workplace: The Impact of AI on Perceived Job Decency and Meaningfulness](https://mlsanity.com/paper/2605.28680/) (2026)
- [More Than Just Access: Generative AI as Communication Intermediary for Blind and Low-Vision Users](https://mlsanity.com/paper/2609.15696/) (2026)
- [Available but Unclaimed: An Empirical Study of Human-AI Synergy](https://mlsanity.com/paper/2609.16793/) (2026)
- [When the AI Leaves the Tailorshop: Measuring What an LLM Advisor Leaves Behind in Complex Problem Solving](https://mlsanity.com/paper/2610.00163/) (2026)

## Abstract (arXiv)

Although recent developments in generative AI have greatly enhanced the capabilities of conversational agents such as Google's Gemini (formerly Bard) or OpenAI's ChatGPT, it's unclear whether the usage of these agents aids users across various contexts. To better understand how access to conversational AI affects productivity and trust, we conducted a mixed-methods, task-based user study, observing 76 software engineers (N=76) as they completed a programming exam with and without access to Bard. Effects on performance, efficiency, satisfaction, and trust vary depending on user expertise, question type (open-ended "solve" vs. definitive "search" questions), and measurement type (demonstrated vs. self-reported). Our findings include evidence of automation complacency, increased reliance on the AI over the course of the task, and increased performance for novices on "solve"-type questions when using the AI. We discuss common behaviors, design recommendations, and impact considerations to improve collaborations with conversational AI.