Artificial Intelligence, Generative AI and Large Language Models: Transforming Technology and Society

Dear Colleagues,

The rapid advancement of Artificial Intelligence (AI), particularly in the domains of Generative AI (GenAI) and Large Language Models (LLMs), has fundamentally transformed how we interact with technology and process information. From ChatGPT and GPT-4 to Claude, Gemini, and emerging multimodal systems, these technologies are reshaping industries, education, research methodologies, and social interactions.

This Special Issue aims to explore the multifaceted impact of AI, GenAI, and LLMs across various domains, addressing the signficant opportunities and challenges that these technologies present. We invite researchers, practitioners, and thought leaders to contribute original research, comprehensive reviews, and innovative applications that will advance our understanding of these transformative technologies.

Topics of Interest Include, but are not limited to, the following:

Technical Advances and Methodologies:

• Novel architectures and training methodologies for Large Language Models;
• Multimodal AI systems integrating text, image, audio, and video;
• Prompt engineering and optimization techniques;
• Fine-tuning and adaptation strategies for domain-specific applications;
• Evaluation metrics and benchmarking for generative AI systems.

Applications and Use Cases:

• AI in education: Personalized learning, automated assessment, and pedagogical applications.
• Healthcare applications: Medical diagnosis, drug discovery, and patient care.
• Creative industries: Content generation, artistic creation, and media production.
• Software engineering: Code generation, debugging, and automated programming.
• Scientific research: Hypothesis generation, literature review automation, and data analysis.

Ethical and Societal Implications:

• AI bias, fairness, and algorithmic accountability;
• Privacy and security concerns in AI systems;
• Intellectual property and copyright issues in generative AI;
• Impact on employment and workforce transformation;
• AI governance, regulation, and policy frameworks.

Human-AI Interaction:

• User experience design for AI-powered systems;
• Trust, transparency, and explainability in AI;
• Collaborative intelligence and human–AI partnerships;
• Psychological and cognitive aspects of AI interactions;
• Digital literacy and AI education.

Technical Challenges and Solutions:

• Hallucination detection and mitigation in LLMs;
• Computational efficiency and environmental impact;
• Data quality, curation, and training methodologies;
• Robustness, reliability, and safety in AI systems;
• Federated learning and distributed AI approaches.

Industry and Economic Impacts:

• Startup ecosystems and AI entrepreneurship;
• Digital transformation strategies.

Prof. Dr. Aneta Poniszewska-Maranda
Guest Editor

Manuscript Submission Information

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