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Electronics
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The Future of AI-Generated Content（AIGC）
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The Future of AI-Generated Content（AIGC）

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2025) | Viewed by 9071

Special Issue Editor

Dr. Ajay Bandi

E-Mail Website
Guest Editor
School of Computer Science and Information Systems, Northwest Missouri State University, Maryville, MO 64468, USA
Interests: generative AI; cybersecurity; secure software systems; spatial computing

Special Issue Information

Dear Colleagues,

Due to the exponential increase in AI-generated content (AIGC) in various industrial applications, this Special Issue aims to explore the current state and future potential of AIGC. It collects high-quality literature reviews, mapping studies, technical, multidisciplinary, and cross-disciplinary advancements in AI content generation.

The Special Issue on “The Future of AI-Generated Content (AIGC)” invites contributions on a variety of topics related to advancements in AI content generation. This includes novel algorithms and models for generating text, images, audio, and video, as well as improvements in natural language processing and understanding for content creation. Submissions may include case studies and applications of AI in content creation across different industries. We also seek papers that discuss methods for assessing the quality and authenticity of AI-generated content, explore human–AI collaboration in content creation, and present comparative studies between human-generated and AI-generated content.

Future directions and emerging trends in AI content generation are also of great interest. Papers that provide predictions for the future development of AI-generated content, cross-disciplinary perspectives on its impact, and innovative uses of AI in generating interactive and immersive content are encouraged. This Special Issue aims to provide a comprehensive overview of the current state and future potential of AI-generated content, fostering a deeper understanding of its possibilities, applications in various domains, and challenges.

The topics include, but are not limited to, the following:

  • Prompt engineering;
  • Prompt effectiveness methods;
  • Algorithms for AIGC for various input and output formats;
  • Quality assessments for AIGC;
  • Metrics for evaluation of AIGC;
  • User interfaces for AIGC;
  • User experience and human factors in AIGC;
  • Security and privacy issues in AIGC;
  • Identifying deepfake content;
  • Fake news identification;
  • AIGC chatbots;
  • Retrieval augmented generation (RAG);
  • Future challenges.

Dr. Ajay Bandi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AIGC
  • generative AI
  • prompt engineering
  • fake news identification
  • AIGC chatbots
  • retrieval augmented generation (RAG)

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Published Papers (3 papers)

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Research

19 pages, 1164 KB  
Article
Improving GPT-Driven Medical Question Answering Model Using SPARQL–Retrieval-Augmented Generation Techniques
by Abdulelah Algosaibi and Abdul Rahaman Wahab Sait
Electronics 2025, 14(17), 3488; https://doi.org/10.3390/electronics14173488 - 31 Aug 2025
Viewed by 189
Abstract
The development of medical question-answering systems (QASs) encounters substantial challenges due to the complexities of medical terminologies and the lack of reliable datasets. The shortcomings of traditional artificial intelligence (AI) driven QAS lead to generating outcomes with a higher rate of hallucinations. In [...] Read more.
The development of medical question-answering systems (QASs) encounters substantial challenges due to the complexities of medical terminologies and the lack of reliable datasets. The shortcomings of traditional artificial intelligence (AI) driven QAS lead to generating outcomes with a higher rate of hallucinations. In order to overcome these limitations, there is a demand for a reliable QAS to understand and process complex medical queries and validate the quality and relevance of its outcomes. In this study, we develop a medical QAS by integrating SPARQL, retrieval-augmented generation (RAG), and generative pre-trained transformer (GPT)-Neo models. Using this strategy, we generate a synthetic dataset to train and validate the proposed model, addressing the limitations of the existing QASs. The proposed QAS was generalized on the MEDQA dataset. The findings revealed that the model achieves a generalization accuracy of 87.26% with a minimal hallucination rate of 0.16. The model outperformed the existing models by leveraging deep learning techniques to handle complex medical queries. The dynamic responsive capability of the proposed model enables it to maintain the accuracy of medical information in a rapidly evolving healthcare environment. Employing advanced hallucination reduction and query refinement techniques can fine-tune the model’s performance. Full article
(This article belongs to the Special Issue The Future of AI-Generated Content（AIGC）)
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18 pages, 433 KB  
Article
A Retrieval-Augmented Generation Method for Question Answering on Airworthiness Regulations
by Tao Zheng, Shiyu Shen and Changchang Zeng
Electronics 2025, 14(16), 3314; https://doi.org/10.3390/electronics14163314 - 20 Aug 2025
Viewed by 321
Abstract
Civil aviation airworthiness regulations are the fundamental basis for the design and operational safety of aircraft. Their provisions exhibit a high degree of specialization, cross-disciplinary complexity, and hierarchical structure. Moreover, the regulations are frequently updated, posing unique challenges for automated question-answering systems. While [...] Read more.
Civil aviation airworthiness regulations are the fundamental basis for the design and operational safety of aircraft. Their provisions exhibit a high degree of specialization, cross-disciplinary complexity, and hierarchical structure. Moreover, the regulations are frequently updated, posing unique challenges for automated question-answering systems. While large language models (LLMs) have demonstrated remarkable capabilities in dialog and reasoning; however, they still face challenges such as difficulties in knowledge updating and a scarcity of high-quality domain-specific datasets when tackling knowledge-intensive tasks in the field of civil aviation regulations. This study introduces a retrieval-augmented generation (RAG) approach that integrates retrieval modules with generative models to enable more efficient knowledge acquisition and updating, encompassing data processing and retrieval-based reasoning. The data processing stage comprises document conversion, information extraction, and document parsing modules. Additionally, a high-quality airworthiness regulation QA dataset was specifically constructed, covering multiple-choice, true/false, and fill-in-the-blank questions, with a total of 4688 entries. The retrieval-based reasoning stage employs vector search and re-ranking strategies, combined with prompt optimization, to enhance the model’s reasoning capabilities in specific airworthiness certification regulation comprehension tasks. A series of experiments demonstrate the effectiveness of the retrieval-augmented generation approach in this domain, significantly improving answer accuracy and retrieval hit rates. Full article
(This article belongs to the Special Issue The Future of AI-Generated Content（AIGC）)
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17 pages, 3538 KB  
Article
Advancing Generative Intelligent Tutoring Systems with GPT-4: Design, Evaluation, and a Modular Framework for Future Learning Platforms
by Siyang Liu, Xiaorong Guo, Xiangen Hu and Xin Zhao
Electronics 2024, 13(24), 4876; https://doi.org/10.3390/electronics13244876 - 11 Dec 2024
Cited by 5 | Viewed by 7214
Abstract
Generative Intelligent Tutoring Systems (ITSs), powered by advanced language models like GPT-4, represent a transformative approach to personalized education through real-time adaptability, dynamic content generation, and interactive learning. This study presents a modular framework for designing and evaluating such systems, leveraging GPT-4’s capabilities [...] Read more.
Generative Intelligent Tutoring Systems (ITSs), powered by advanced language models like GPT-4, represent a transformative approach to personalized education through real-time adaptability, dynamic content generation, and interactive learning. This study presents a modular framework for designing and evaluating such systems, leveraging GPT-4’s capabilities to enable Socratic-style interactions and personalized feedback. A pilot implementation, the Socratic Playground for Learning (SPL), was tested with 30 undergraduate students, focusing on foundational English skills. The results showed significant improvements in vocabulary, grammar, and sentence construction, alongside high levels of engagement, adaptivity, and satisfaction. The framework employs lightweight JSON structures to ensure scalability and versatility across diverse educational contexts. Despite its promise, challenges such as computational demands and content validation highlight the main areas for future refinement. This research establishes a foundational approach for advancing Generative ITSs, offering key insights into personalized learning and the broader potential of Generative AI in education. Full article
(This article belongs to the Special Issue The Future of AI-Generated Content（AIGC）)
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