Advanced Development and Applications of AI-Generated Content (AIGC)

Topic Information

Dear Colleagues,

The rapid evolution of Artificial Intelligence Generated Content (AIGC), particularly driven by large-scale multimodal models, is transforming the way we create, interact with, and consume digital content. From technical architectures to user-centric applications, AIGC has shown immense potential across a wide range of domains—such as automated media production, smart design tools, Artificial Intelligence (AI)-powered content platforms, and even digital behavioral interactions. This Topic seeks to explore both the technical foundations and societal implications of AIGC. We aim to collect contributions that not only propose novel algorithms, models, and systems, but also analyze how these technologies impact human behavior, communication, and digital culture. We welcome submissions from various perspectives, including engineering, computer science, information systems, behavioral sciences, and interdisciplinary studies.

Topics of interest include, but are not limited to the following:

• New architectures and frameworks for AIGC systems;
• Multimodal content generation: text, image, audio, video, and 3D;
• Development and deployment of large-scale multimodal foundation models;
• Human-AI collaboration and user perception in AIGC environments;
• AIGC applications in engineering, media, education, and intelligent systems;
• Behavioral and psychological impacts of interacting with AI-generated content;
• Information dissemination, trust, and ethics in AI-powered content ecosystems;
• Privacy, security, and copyright challenges in AIGC.

We particularly encourage work that bridges theory and practice, technology and society, and algorithm and application. Researchers and practitioners from academia, industry, and public sectors are warmly invited to contribute.

Prof. Dr. Bolong Zheng
Dr. Qing Xie
Dr. You Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	24.5 Days	CHF 1800	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit

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Open AccessArticle

A Comparative Performance Analysis of Locally Deployed Large Language Models Through a Retrieval-Augmented Generation Educational Assistant Application for Textual Data Extraction

by Amitabh Mishra and Nagaraju Brahmanapally

AI 2025, 6(6), 119; https://doi.org/10.3390/ai6060119 - 6 Jun 2025

Viewed by 830

Abstract

Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems. Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries [...] Read more.

Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems. Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries related to course details and other essential academic information, and investigates its performance by testing it on several locally deployed large language models. By leveraging multiple LLM architectures, we evaluate performance of the models under test in terms of context length, embedding size, computational efficiency, and relevance of responses. Results: The experimental analysis obtained by this research, which builds on recent comparative studies, reveals that while larger models achieve higher relevance scores, they incur greater response times than smaller, more efficient models. Conclusions: The findings underscore the importance of balancing accuracy and efficiency for real-time educational applications. Overall, this work contributes to the field by offering insights into optimal RAG configurations and practical guidelines for deploying AI-powered educational assistants. Full article

(This article belongs to the Topic Advanced Development and Applications of AI-Generated Content (AIGC))

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