Editorial Note to Special Issue “Generative AI and Its Transformative Potential”

In recent years, generative artificial intelligence (AI) has emerged as a powerful paradigm capable of transforming both scientific research and business applications. Its rapid development—driven by advances in machine learning and deep learning—has enabled the creation of models that not only automate repetitive tasks but also generate new content and augment human creativity.

This Special Issue of Electronics, titled “Generative AI and Its Transformative Potential”, explores the role of generative AI across various scientific domains and industry sectors. Generative AI offers a wide range of opportunities for innovation and problem-solving—from simplifying product design processes in manufacturing to generating synthetic data for training intelligent systems in areas such as quality control, autonomous systems, and healthcare.

This Issue features contributions from leading researchers and practitioners working on the theoretical, technological, and applied aspects of generative AI. It emphasizes interdisciplinary perspectives and real-world implementations, addressing both the opportunities and challenges posed by this rapidly evolving technology.

Of the numerous submissions received, fourteen high-quality papers were selected for inclusion following a rigorous peer-review process. These contributions span a variety of application areas, showcasing the diverse potential of generative AI. For clarity and coherence, the selected papers are organized into the following three thematic clusters: (1) foundational models and frameworks for generative AI; (2) applications in education, language, and human-centered systems; and (3) creative and industrial use cases.

The first cluster includes papers that explore the architectures, development tools, and evaluation techniques associated with generative AI systems. Topics include the application of large language models, transformer-based architectures, and hybrid AI techniques for knowledge extraction and content generation. The following five papers are included:

• “AI-Assisted Programming Tasks Using Code Embeddings and Transformers” explores how code embeddings and transformer architectures can support programmers, automate coding tasks, and enhance software development through AI-generated assistance.
• “Augmenting Large Language Models with Rules for Enhanced Domain-Specific Interactions: The Case of Medical Diagnosis” proposes a hybrid framework that enriches large language models with rule-based knowledge for medical diagnosis, thereby improving precision and contextual relevance in clinical decision support.
• “The Genesis of AI by AI Integrated Circuit: Where AI Creates AI” presents a visionary concept of AI-generated AI circuits, discussing conceptual frameworks and potential hardware–software synergies to support autonomous AI design.
• “Extension of Interval-Valued Hesitant Fermatean Fuzzy TOPSIS for Evaluating and Benchmarking of Generative AI Chatbots” introduces a novel fuzzy multi-criteria decision-making method to evaluate generative AI chatbots, enabling objective benchmarking based on user preferences and system capabilities.
• “Understanding Factors Influencing Generative AI Use Intention: A Bayesian Network-Based Probabilistic Structural Equation Model Approach” employs a probabilistic SEM using Bayesian networks to identify key factors influencing users’ intentions to adopt generative AI tools, integrating behavioural science with data-driven modelling.

The second group focuses on the use of generative AI in personalized education, language learning, and emotion-aware systems. These studies illustrate how generative models can enhance engagement, tailor content to learner profiles, and facilitate human–machine interaction. The five papers included in this cluster are as follows:

6.

“Pre-Service Teachers’ Assessment of ChatGPT’s Utility in Higher Education: SWOT and Content Analysis” investigates pre-service teachers’ perceptions of ChatGPT in higher education and instructional settings using a combination of SWOT analysis and qualitative methods.

7.

“Framework for Integrating Generative AI in Developing Competencies for Accounting and Audit Professionals” introduces a framework that leverages generative AI to support skills development in the accounting and auditing professions, focusing primarily on personalized learning and scenario-based training.

8.

“A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource Languages” reviews recent advancements and ongoing challenges in NLP, with particular attention given to legal informatics and low-resource languages, where generative models face unique linguistic and contextual constraints.

9.

“Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI” draws philosophical parallels to examine the emergence of cultural bias in generative AI outputs and propose strategies to mitigate such bias during training and deployment.

10.

“Web Application for Retrieval-Augmented Generation: Implementation and Testing” presents a retrieval-augmented generation (RAG) system that integrates search and generation to improve factual consistency and traceability in AI-generated content.

The final cluster presents practical implementations of generative AI across domains such as visual arts, smart manufacturing, media content generation, and social simulations. The following four papers demonstrate how AI can co-create content and improve decision-making in complex environments:

11.

“Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies” applies GANs to augment datasets of digits and characters from standardized ship markings, supporting enhanced recognition and automation in maritime inspection systems.

12.

“Generative AI-Driven Data Augmentation for Crack Detection in Physical Structures” demonstrates the use of generative models to synthesize data for training crack detection algorithms, advancing structural health monitoring in civil engineering.

13.

“Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models” presents experiments using diffusion models to reconstruct and analyse head rotation under varying lighting conditions, highlighting generative AI’s strength in complex visual tasks.

14.

“Novel Learning Framework with Generative AI X-ray Images for Deep Neural Network-Based X-Ray Security Inspection of Prohibited Items” proposes a new training framework that utilises synthetic X-ray images generated by AI to improve prohibited-item detection in security systems, using YOLO-based architectures.

We hope this Special Issue will serve as a valuable reference for researchers, developers, and decision-makers seeking to leverage generative AI in their respective fields. We also hope that these contributions will inspire further research, foster interdisciplinary collaboration, and promote the responsible deployment of generative AI technologies for the benefit of society.

As generative AI continues to evolve, future editions of Electronics will undoubtedly revisit this topic, offering expanded scopes and new perspectives.