arXiv:2609.15624v1 Announce Type: cross
Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We...
By Daniele Veri'
arXiv:2606. 28749v1 Announce Type: cross Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no validated method distinguishes the qualitatively different ways students rely on them.
By Shahin Hossain
arXiv:2412. 19754v4 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) is transforming the nature of work, yet there is limited empirical evidence on how it affects demand for human skills.
By Elina M\"akel\"a, Matthew Bone, Mareike Sehrer, Farah Nanji, Fabian Stephany
arXiv:2605. 21629v2 Announce Type: replace-cross Abstract: How much have students' ordinary learning processes shifted in response to generative AI, and how does that affect their durable learning outcomes?
By Sina Rismanchian, Hasan Uzun, Jeffrey Matayoshi, Eric Cosyn, Eyad Kurd-Misto
The study investigates how generative AI (GenAI) affects student learning in AI-related courses, using survey data from 118 students across 12 courses. Four distinct user clusters were identified—high-use, light-use, and two moderate-use groups—each showing varying benefits and reliance patterns. The research highlights that early reliance, evaluation literacy, and instructor policies significantly influence perceived academic benefits and negative impacts, underscoring the need for institutional policies to address inequities in AI use.
By Lydia Manikonda, Mei Si, Sirajam Munira, Oshani Seneviratne, Kristin Bennett
arXiv:2607. 01810v1 Announce Type: cross Abstract: Open-source projects depend on a steady inflow of newcomers.
By Weiwei Xu, Xuanning Cui, Hengzhi Ye, Minghui Zhou