AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositions

2025-09-14

AI Summary

Current AIGC cross-domain research suffers from systemic fragmentation and disciplinary silos. To address this, this project brings together 16 interdisciplinary scholars from education, public health, digital marketing, and related fields—marking the first coordinated effort of its kind. We establish an integrated research framework encompassing training methodologies, detection techniques, multimodal content analysis, and cross-platform diffusion modeling. Through empirical studies across multiple domains, we systematically identify core challenges along three dimensions: societal impact, technical bottlenecks, and governance gaps—yielding theoretically grounded yet practice-oriented research questions and technology development pathways. The project fills a critical void in cross-domain AIGC research and delivers a reusable methodological framework. It provides foundational support for evidence-based policy formulation, responsible technology governance, and future interdisciplinary scholarship.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Exploring AIGC progress and challenges across domains
• Providing cross-domain perspective on trends and societal impacts
• Addressing key technical challenges and future research directions

Innovation

• Cross-domain expert collaboration for AIGC analysis
• Training techniques and detection methods review
• Research propositions for future technical challenges