Large Language Models Applications in Computational Creativity

Dear Colleagues,

The emergence of Large Language Models (LLMs) has transformed the landscape of computational creativity, bridging the gap between art, science, and artificial intelligence. From AI-assisted writing, music, and design to scientific discovery and creative problem-solving, LLMs are redefining how machines contribute to human creativity. As these models evolve, they raise fundamental questions about authorship, originality, human-AI collaboration, ethical implications, and the rise in synthetic content. This Special Issue aims to explore the capabilities, applications, and challenges of LLMs in fostering computational creativity while also addressing the detection and mitigation of AI-generated misinformation and fake content.

This Special Issue focuses on the role of LLMs in computational creativity, aligning with the broader scope of Electronics, which includes AI-driven innovation, human–computer interaction, and intelligent systems. We invite contributions that explore both theoretical advancements and practical implementations of LLMs in creative processes. Topics may include generative AI techniques, multimodal creativity, knowledge representation, and ethical considerations in AI-generated content. Additionally, we encourage research on detection methods for AI-generated misinformation, deepfakes, and the verification of content authenticity.

We welcome original research articles and reviews on (but not limited to) the following topics:

• Generative AI for Creativity: AI-assisted storytelling, poetry, music, design, and visual arts;
• LLMs in Scientific and Technological Innovation: AI-driven hypothesis generation, algorithmic creativity, and research ideation;
• Multimodal and Interactive Creativity: Combining LLMs with visual, audio, and game design models;
• Human-AI Co-Creation: Collaborative AI systems for creative professionals;
• Ethical, Philosophical, and Legal Aspects: Bias, originality, authorship, and IP rights in AI-generated works;
• Detection and Mitigation of Fake Content: Identifying AI-generated misinformation, deepfake detection, and content verification;
• Benchmarking and Evaluation: Metrics for measuring AI creativity, authenticity, and societal impact.

We encourage interdisciplinary research that explores how LLMs enhance, challenge, or redefine creative expression across domains while also addressing the risks associated with AI-generated misinformation. This Special Issue will provide insights into new frontiers of AI-driven creativity, shaping the future of human–machine collaboration and content authenticity.

We look forward to your contributions!

Dr. Garima Agrawal
Dr. Yuli Deng
Dr. Amardeep Kaur
Guest Editors

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