| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03021 | |
| Number of page(s) | 8 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403021 | |
| Published online | 06 April 2026 | |
AIGC Text Generation: Technological Evolution, Application Scenarios, and Future Challenges
Department of Computer and Information Engineering, Shanxi Institute of Energy, Jinzhong, China
* Corresponding author’s email: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Artificial Intelligence Generated Content (AIGC) refers to technology that uses artificial intelligence to automatically or semi-automatically generate digital content such as text, images, audio, and video. In recent years, generative AI technologies such as large language models (LLMs) based on the Transformer architecture and diffusion models have rapidly developed, driving the widespread application of AIGC in the field of text generation. This article provides a systematic review of the technological evolution, key applications, evaluation methods, and challenges of AIGC text generation. First, it outlines the development trajectory of AIGC technologies, from rule-based and statistical methods to large language model-based approaches. Next, it analyzed its application scenarios and effects in fields such as education, journalism, and cultural creativity; then, it summarized a multi-dimensional evaluation system, including automatic evaluation, human evaluation, AI-generated text detection, and watermarking technology, and explored the AIGC content labeling solutions that have emerged to address abuse risks and their effectiveness; Finally, the paper discusses the challenges of AIGC in terms of technical reliability, bias and fairness, ethical compliance, and social impact, and provides an outlook on future research directions. This article aims to provide a systematic reference for the research and practice of AIGC text generation.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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