AI-generated content in cross-domain applications: Research trends, challenges and propositions

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems Pub Date : 2025-10-10 DOI:10.1016/j.knosys.2025.114634

Jianxin Li , Liang Qu , Taotao Cai , Zhixue Zhao , Nur Al Hasan Haldar , Aneesh Krishna , Xiangjie Kong , Flavio Romero Macau , Tanmoy Chakraborty , Aniket Deroy , Binshan Lin , Karen Blackmore , Nasimul Noman , Jingxian Cheng , Ningning Cui , Jianliang Xu

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引用次数: 0

Abstract

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.

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跨领域应用中的人工智能生成内容：研究趋势、挑战与主张

人工智能生成内容（AIGC）已经迅速崛起，它能够生成不同形式的内容，包括文本、图像、视频和其他形式，这些内容可以达到与人类创建的内容相似的质量。因此，AIGC现已广泛应用于数字营销、教育和公共卫生等各个领域，并通过提高内容创建效率和改善信息传递显示出良好的效果。然而，很少有研究探讨不同领域AIGC的最新进展和新挑战。为了弥补这一差距，本文汇集了来自多个学科的16位学者，从跨领域的角度对AIGC的趋势和挑战进行了探讨。具体来说，本文的贡献有三个方面：(1)它首先提供了AIGC的更广泛概述，涵盖了生成式人工智能的训练技术、检测方法，以及人工智能生成内容在数字平台上的传播和使用。(2)然后介绍了AIGC在不同领域的社会影响，以及在这些背景下采用的现有方法的回顾。(3)最后，讨论了关键技术挑战，提出了指导未来工作的研究主张。通过这些贡献，这篇愿景论文试图为读者提供一个关于AIGC的跨领域视角，提供对其当前研究趋势、持续挑战和未来方向的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Knowledge-Based Systems 工程技术-计算机：人工智能

CiteScore

14.80

自引率

12.50%

发文量

1245

审稿时长

7.8 months

期刊介绍： Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.