# Investigating the Effects of LLM Use on Critical Thinking Under Time Constraints: Access Timing and Time Availability

arXiv: https://arxiv.org/abs/2603.08849 · HTML: https://mlsanity.com/paper/2603.08849/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 10 of its 93 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 10 of 10)

- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [Modulating Language Model Experiences through Frictions](https://mlsanity.com/paper/2407.12804/) (2024)
- [Protecting Human Cognition in the Age of AI](https://mlsanity.com/paper/2502.12447/) (2025)
- [Close to Human-Level Agreement: Tracing Journeys of Violent Speech in Incel Posts with GPT-4-Enhanced Annotations](https://mlsanity.com/paper/2401.02001/) (2024)
- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [Coach not crutch: Evidence that AI can improve writing skill despite reducing effort](https://mlsanity.com/paper/2502.02880/) (2025)
- [Who's the Leader? Analyzing Novice Workflows in LLM-Assisted Debugging of Machine Learning Code](https://mlsanity.com/paper/2505.08063/) (2025)
- [The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI](https://mlsanity.com/paper/2506.11015/) (2025)

## Built on by (most cited first, 4 of 4)

- [Offloading Score: Measuring AI Reliance Through Counterfactual Workflows](https://mlsanity.com/paper/2605.29392/) (2026)
- [AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains](https://mlsanity.com/paper/2604.19751/) (2026)
- [Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education](https://mlsanity.com/paper/2605.15850/) (2026)
- [Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions](https://mlsanity.com/paper/2608.02491/) (2026)

## Abstract (arXiv)

The impact of large language models (LLMs) on critical thinking has provoked growing attention, yet this impact on actual performance may not be uniformly negative or positive. Particularly, the role of time -- the temporal context under which an LLM is provided -- remains overlooked. In a between-subjects experiment (n=393), we examined two types of time constraints for a critical thinking task requiring participants to make a reasoned decision for a real-world scenario based on diverse documents: (1) LLM access timing -- an LLM available only at the beginning (early), throughout (continuous), near the end (late), or not at all (no LLM), and (2) time availability -- insufficient or sufficient time for the task. We found a temporal reversal: LLM access from the start (early, continuous) improved performance under time pressure but impaired it with sufficient time, whereas beginning the task independently (late, no LLM) showed the opposite pattern. These findings demonstrate that time constraints fundamentally shape whether an LLM augments or undermines critical thinking, making time a central consideration when designing LLM support and evaluating human-AI collaboration in cognitive tasks.