Effects of generative artificial intelligence on cognitive effort and task performance: study protocol for a randomized controlled experiment among college students.
Youjie Chen, Yingying Wang, Torsten Wüstenberg, Rene F Kizilcec, Yiwen Fan, Yanfei Li, Bin Lu, Meng Yuan, Junlai Zhang, Ziyue Zhang, Pascal Geldsetzer, Simiao Chen, Till Bärnighausen
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引用次数: 0
Abstract
Background: The advancement of generative artificial intelligence (AI) has shown great potential to enhance productivity in many cognitive tasks. However, concerns are raised that the use of generative AI may erode human cognition due to over-reliance. Conversely, others argue that generative AI holds the promise to augment human cognition by automating menial tasks and offering insights that extend one's cognitive abilities. To better understand the role of generative AI in human cognition, we study how college students use a generative AI tool to support their analytical writing in an educational context. We will examine the effect of using generative AI on cognitive effort, a major aspect of human cognition that reflects the extent of mental resources an individual allocates during the cognitive process. We will also examine the effect on writing performance achieved through the human-generative AI collaboration.
Methods: This study is a randomized controlled lab experiment that compares the effects of using generative AI (intervention group) versus not using it (control group) on cognitive effort and writing performance in an analytical writing task designed as a hypothetical writing class assignment for college students. During the experiment, eye-tracking technology will monitor eye movements and pupil dilation. Functional near-infrared spectroscopy (fNIRS) will collect brain hemodynamic responses. A survey will measure individuals' perceptions of the writing task and their attitudes on generative AI. We will recruit 160 participants (aged 18-35 years) from a German university where the research will be conducted.
Discussion: This trial aims to establish the causal effects of generative AI on cognitive effort and task performance through a randomized controlled experiment. The findings aim to offer insights for policymakers in regulating generative AI and inform the responsible design and use of generative AI tools.
Trial registration:
Clinicaltrials: gov NCT06511102. Registered on July 15, 2024. https://clinicaltrials.gov/study/NCT06511102.
期刊介绍：
Trials is an open access, peer-reviewed, online journal that will encompass all aspects of the performance and findings of randomized controlled trials. Trials will experiment with, and then refine, innovative approaches to improving communication about trials. We are keen to move beyond publishing traditional trial results articles (although these will be included). We believe this represents an exciting opportunity to advance the science and reporting of trials. Prior to 2006, Trials was published as Current Controlled Trials in Cardiovascular Medicine (CCTCVM). All published CCTCVM articles are available via the Trials website and citations to CCTCVM article URLs will continue to be supported.
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