The study investigates how large language model (LLM) advisors affect complex problem‑solving in a simulated clothing‑factory setting. Two preregistered experiments (N=200 and N=198) found that participants with AI support reported higher confidence and understanding, expended less effort, and in some cases achieved better performance or avoided bankruptcy. Within the AI‑supported group, more frequent changes to the AI’s recommendations were linked to improved unaided performance and knowledge.
By Robin Welsch
arXiv:2608. 07494v1 Announce Type: cross Abstract: AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan a three-day Vienna trip'', ``solve the attached mathematical problem'', ``draft an email to inquire review progress'', etc.
By Yiqun Zhang, Yunfan Zhang, Mingjie Zhao, Sen Feng, Yiu-ming Cheung
arXiv:2609.22249v1 Announce Type: new
Abstract: This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice...
By Erfan Loweimi, Hadi Daneshvar, Samira Loveymi, Samir Ouelha, Zhengjun Yue, Hajar Mozaffar, Saturnino Luz
arXiv:2608.22993v1 Announce Type: new
Abstract: Students increasingly use LLMs as tutors for coursework and problem solving. Little is known about the level of assistance LLMs provide when students u...
By Suhyeon Lee, Juneha Baek, Jaehyeong Park, Donghyuk Shin
The study examined the impact of expert‑verified AI‑generated study materials on first‑year university economics students. In a two‑cohort difference‑in‑differences design, half of the 170 students received podcasts, FAQs, and quiz‑based guides that were produced by a source‑grounded model and checked by a graduate teaching assistant. The intervention produced a 2.34‑mark advantage on a 50‑mark component, reduced the share of marks below the upper‑second classification boundary by 24.7 percentage points, and had the largest gains for students in the bottom quintile. Interviews with 36 students suggested that the verification label encouraged engagement while preserving critical scrutiny of the AI output.
By Canh Thien Dang, An Nguyen
arXiv:2607. 21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems.
By Verona Teo, Raghav Jain, Tobias Gerstenberg, Max Kleiman-Weiner
arXiv:2606. 07462v1 Announce Type: new Abstract: As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long-horizon coding tasks and even autonomous experiment execution.
By Jiayu Wang, Weijiang Lv, Bowen Fu, Jing Fu, Jiayi Song, Lingyu Zhang, Lanxuan Xue, Luodi Chen, Zepeng Xin, Kaiyu Li, Xiangyong Cao
arXiv:2607. 19941v1 Announce Type: cross Abstract: As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust and successful adoption.
By Kathrin Paimann, Elizangela Valarini, Sebastian Juhl
arXiv:2609.16793v1 Announce Type: cross
Abstract: People increasingly reason with large language models (LLMs), yet complementary capabilities do not guarantee outperforming both components. In a bet...
By Robin Welsch, Michelle Rausch, Pascal Knierim, Thomas Kosch, Jochen Kuhn, Albrecht Schmidt, Daniela Fernandes
arXiv:2608. 03283v1 Announce Type: new Abstract: Identifying promising scientific ideas remains an important challenge in research practice.
By Zhiyao Cui, Qianyi Wang, Haoyang Yan, Yiqun Zhang, Siyue Ren, Hangfan Zhang, Zelin Tan, Hao Li, Chunjiang Mu, Dexian Cai, Shao Zhang, Chen Zhang, Meng Li, Jianan Chai, Yuting Fan, Zichao Ye, Xiaolei Yang, Xinyao Lu, Yuyang Yu, Wenjie Lou, Xiaosong Wang, Fenghua Ling, Shiyang Feng, Mao Su, Qiaosheng Zhang, Bo Zhang, Yang Chen, Lei Bai, Shuyue Hu
arXiv:2609.16436v1 Announce Type: cross
Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the...
By Jiayue Gaveal Fan, Arul Murugan, Shreyas Krishnan, Abhishek Nagaraj
arXiv:2606. 05178v1 Announce Type: cross Abstract: As AI-driven product development accelerates, the bottleneck is shifting from how we build to what we build.
By Tim Dorn, Saara A. Khan, Julie Mumford