Skip to content
#generative ai Dataset Open access

A five-month prospectives study of frequency of generative AI use and workplace loneliness

Sep 2026 · Figshare

Abstract

The study employed a 5-month prospective design from February to July 2026. A total of 2,000 participants were recruited through Rakuten Insight, Inc. (https://insight.rakuten.co.jp/), a large internet survey company, from its panel of approximately 2.2 million registered users. Eligibility criteria were being a man or woman aged 20–59 years, employed by a company or organization in Japan, working full-time for at least 35 hours per week. Exclusion criteria was individuals who were on sick leave or maternity leave. Stratified sampling was used to obtain equal numbers of participants by sex and age group: 20–29, 30–39, 40–49, and 50 years or older. Participants completed a self-administered online questionnaire assessing generative AI use, workplace loneliness, and demographic and occupational characteristics. A follow-up online questionnaire survey was conducted at 5 months later, asserting workplace loneliness again. We did not ask questions about workplace loneliness to baseline respondents who retired or changed employer during the follow-up.A total of 1,749 participants (87.5%) responded to the follow-up survey. Forty-five participants who quit or retired from employment were not asked about generative AI use or workplace loneliness. Data at follow-up were available for the remaining 1,704 (85.0%) participants.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.