Editorial Note to Special Issue “Generative AI and Its Transformative Potential”
- “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.
- 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.
- 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.
Author Contributions
Conflicts of Interest
List of Contributions
- Kotsiantis, S.; Verykios, V.; Tzagarakis, M. AI-Assisted Programming Tasks Using Code Embeddings and Transformers. Electronics 2024, 13, 767. https://doi.org/10.3390/electronics13040767.
- Panagoulias, D.; Virvou, M.; Tsihrintzis, G. Augmenting Large Language Models with Rules for Enhanced Domain-Specific Interactions: The Case of Medical Diagnosis. Electronics 2024, 13, 320. https://doi.org/10.3390/electronics13020320.
- Baungarten-Leon, E.; Ortega-Cisneros, S.; Abdelmoneum, M.; Vidana Morales, R.; Pinedo-Diaz, G. The Genesis of AI by AI Integrated Circuit: Where AI Creates AI. Electronics 2024, 13, 1704. https://doi.org/10.3390/electronics13091704.
- Ilieva, G. Extension of Interval-Valued Hesitant Fermatean Fuzzy TOPSIS for Evaluating and Benchmarking of Generative AI Chatbots. Electronics 2025, 14, 555. https://doi.org/10.3390/electronics14030555.
- Kim, C. Understanding Factors Influencing Generative AI Use Intention: A Bayesian Network-Based Probabilistic Structural Equation Model Approach. Electronics 2025, 14, 530. https://doi.org/10.3390/electronics14030530.
- Markos, A.; Prentzas, J.; Sidiropoulou, M. Pre-Service Teachers’ Assessment of ChatGPT’s Utility in Higher Education: SWOT and Content Analysis. Electronics 2024, 13, 1985. https://doi.org/10.3390/electronics13101985.
- Anica-Popa, I.; Vrîncianu, M.; Anica-Popa, L.; Cișmașu, I.; Tudor, C. Framework for Integrating Generative AI in Developing Competencies for Accounting and Audit Professionals. Electronics 2024, 13, 2621. https://doi.org/10.3390/electronics13132621.
- Krasadakis, P.; Sakkopoulos, E.; Verykios, V. A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource Languages. Electronics 2024, 13, 648. https://doi.org/10.3390/electronics13030648.
- Karpouzis, K. Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI. Electronics 2024, 13, 1457. https://doi.org/10.3390/electronics13081457.
- Radeva, I.; Popchev, I.; Doukovska, L.; Dimitrova, M. Web Application for Retrieval-Augmented Generation: Implementation and Testing. Electronics 2024, 13, 1361. https://doi.org/10.3390/electronics13071361.
- Abdulraheem, A.; Suleiman, J.; Jung, I. Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies. Electronics 2023, 12, 3668. https://doi.org/10.3390/electronics12173668.
- Kim, J.; Seon, J.; Kim, S.; Sun, Y.; Lee, S.; Kim, J.; Hwang, B.; Kim, J. Generative AI-Driven Data Augmentation for Crack Detection in Physical Structures. Electronics 2024, 13, 3905. https://doi.org/10.3390/electronics13193905.
- Asperti, A.; Colasuonno, G.; Guerra, A. Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models. Electronics 2024, 13, 3091. https://doi.org/10.3390/electronics13153091.
- Kim, D.; Kang, J. Novel Learning Framework with Generative AI X-Ray Images for Deep Neural Network-Based X-Ray Security Inspection of Prohibited Items Detection with You Only Look Once. Electronics 2025, 14, 1351. https://doi.org/10.3390/electronics14071351.
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Ilieva, G.; Tsihrintzis, G.A. Editorial Note to Special Issue “Generative AI and Its Transformative Potential”. Electronics 2025, 14, 1925. https://doi.org/10.3390/electronics14101925
Ilieva G, Tsihrintzis GA. Editorial Note to Special Issue “Generative AI and Its Transformative Potential”. Electronics. 2025; 14(10):1925. https://doi.org/10.3390/electronics14101925
Chicago/Turabian StyleIlieva, Galina, and George A. Tsihrintzis. 2025. "Editorial Note to Special Issue “Generative AI and Its Transformative Potential”" Electronics 14, no. 10: 1925. https://doi.org/10.3390/electronics14101925
APA StyleIlieva, G., & Tsihrintzis, G. A. (2025). Editorial Note to Special Issue “Generative AI and Its Transformative Potential”. Electronics, 14(10), 1925. https://doi.org/10.3390/electronics14101925