Introduction

Dr. Shih-yun Lu is a distinguished academic and researcher currently serving as a Professor in the Department of Digital Content and Technology at National Taichung University of Education. With an interdisciplinary background spanning art, design, artificial intelligence, and digital technology, Dr. Lu has established a significant research profile in the fields of deep learning, AI-assisted creativity, computational aesthetics, human–AI interaction, and natural language processing (NLP), with particular emphasis on how large language models (LLMs) can enhance creative thinking, semantic understanding, multimodal interaction, and AI-assisted artistic production.

Dr. Lu received a Master’s degree in Stage Design from Accademia di Belle Arti di Brera in 1998, and later earned a Ph.D. in Design and Arts from the School of Design at University of Leeds in 2008. His doctoral studies were supported by a prestigious Ministry of Education scholarship awarded by the Taiwanese government in 2003.

Since joining National Taichung University of Education in 2008, Dr. Lu has played an important role in advancing interdisciplinary education and research integrating artificial intelligence with digital content and creative design. He was appointed as an Assistant Professor in the Department of Digital Content Technology in 2008, promoted to Associate Professor in 2013, and later assumed his current professorship position. Throughout his academic career, he has continuously promoted the convergence of AI technologies, digital art, computational media, and intelligent human-centered design.

Dr. Lu’s research focuses primarily on deep learning, computer vision, generative artificial intelligence, AI-driven aesthetic evaluation, prompt engineering, computational creativity, human–computer interaction, natural language processing, and AI applications in digital art and cultural heritage. His recent studies particularly emphasize large language models (LLMs), generative AI co-creation, multimodal AI systems, AI-assisted image generation, semantic understanding, explainable AI frameworks, and human–AI collaborative methodologies for artistic and educational applications.

In the field of deep learning and AI research, Dr. Lu has led multiple interdisciplinary projects involving computer vision–based fashion recognition, AI-generated image aesthetics, multimodal learning systems, semantic segmentation, intelligent prompt optimization, and human-centered AI design. His work integrates state-of-the-art deep learning architectures such as transformer-based models, large language models, generative AI systems, image segmentation networks, and natural language processing techniques to explore innovative approaches to digital creativity, intelligent visual analysis, and semantic human–AI communication.

Dr. Lu has also been actively involved in the development of AI-enhanced educational methodologies within STEAM education. By integrating generative AI, machine learning, natural language processing, and computational thinking into creative practice, he has designed interdisciplinary courses that cultivate innovation, critical thinking, semantic reasoning, and human–AI collaborative capabilities among students. His teaching areas include Artificial Intelligence and Art, Deep Learning Applications in Digital Content, Large Language Model Applications, New Media Art Creation, Human–Computer Interaction, Computational Aesthetics, and AI-Assisted Creative Design.

In addition to his teaching and research activities, Dr. Lu has directed multiple research and educational projects funded by Taiwan’s Ministry of Education and the National Science and Technology Council (NSTC). These projects focus on AI-assisted learning systems, intelligent creative technologies, digital cultural innovation, NLP-driven semantic systems, and AI-driven aesthetic evaluation frameworks aimed at improving educational quality and advancing interdisciplinary AI research.

Dr. Lu currently leads the AI-New Media Art Lab, an advanced interdisciplinary research facility dedicated to artificial intelligence, deep learning, natural language processing, digital creativity, and computational media innovation. The laboratory is equipped with state-of-the-art AI computing resources and focuses on research areas such as generative AI, large language models, computer vision, multimodal interaction, computational aesthetics, intelligent visual systems, semantic AI, and AI-assisted creative production. The lab fosters collaboration among researchers from diverse disciplines, including AI engineering, digital art, design, human–computer interaction, and computational linguistics, to explore the future integration of artificial intelligence and creative industries.

Dr. Lu’s extensive publication record in internationally indexed journals, including Scopus and SSCI publications, demonstrates his substantial contributions to AI-driven creativity, human–AI collaboration, NLP-enhanced creative systems, and intelligent digital content research. Through his ongoing research, leadership, and interdisciplinary innovation, Dr. Lu continues to contribute to the advancement of deep learning, large language models, and artificial intelligence applications in digital art, education, and computational media, shaping new possibilities for the future of human-centered AI technologies.

Areas of Expertise

• Artificial Intelligence in Art and Design: Focused on exploring AI’s role in the creative process, particularly in visual arts, computational creativity, design optimization, AI-generated imagery, prompt engineering, and AI-driven aesthetic evaluation.
• Natural Language Processing (NLP) and Large Language Models (LLMs): Specializing in natural language processing and semantic AI systems, with a focus on how large language models (LLMs) enhance human–AI interaction, creative collaboration, multimodal content generation, semantic understanding, prompt optimization, and AI-assisted artistic and educational applications.
• Deep Learning and Computer Vision: Expertise in deep learning architectures, image recognition, semantic segmentation, multimodal learning systems, and intelligent visual analysis for applications in digital content, fashion recognition, and computational aesthetics.
• STEAM Education: Committed to integrating arts into STEM education, emphasizing creativity, computational thinking, aesthetics, and interdisciplinary innovation within AI-enhanced learning environments.
• Human–AI Interaction and Computational Creativity: Investigating collaborative relationships between humans and intelligent systems, particularly in AI co-creation, explainable AI, computational aesthetics, and human-centered AI design.
• Digital Cultural and Creative Industries: Investigating the intersection of technology and culture, particularly how AI, digital media, and intelligent systems can support cultural heritage preservation, digital storytelling, and creative industry innovation.
• AI-Assisted Educational Technologies: Developing AI-enhanced pedagogical systems and interdisciplinary learning methodologies that integrate generative AI, machine learning, and creative practice into digital art and design education.

Professional Experience

• Deep Learning and Artificial Intelligence: Specialized in deep learning architectures, generative artificial intelligence, and machine learning applications, with research focusing on multimodal AI systems, neural networks, transformer-based models, and intelligent visual computing.
• Computer Vision and Image Analysis: Expertise in computer vision technologies, including image segmentation, object detection, semantic recognition, visual feature extraction, and AI-driven image understanding for digital content and creative applications.
• Generative AI and Computational Creativity: Focused on generative AI systems for creative production, prompt engineering, AI-assisted image generation, computational aesthetics, and human–AI co-creation frameworks that integrate artistic creativity with intelligent algorithms.
• Human–AI Interaction and Explainable AI: Researching human-centered artificial intelligence, explainable AI systems, AI-assisted decision-making, and interactive intelligent interfaces that enhance collaboration between humans and AI technologies.
• AI in Art, Design, and Digital Media: Investigating the application of artificial intelligence in digital art, design innovation, aesthetic evaluation, and creative media production, including AI-assisted illustration, generative visual systems, and intelligent creative workflows.
• Multimodal Learning and Natural Language Processing: Experienced in integrating multimodal AI approaches involving text, image, and semantic understanding, including natural language processing, semantic analysis, and AI-driven content generation systems.
• AI-Enhanced STEAM Education: Committed to advancing AI-integrated STEAM education through interdisciplinary teaching methodologies that combine computational thinking, creativity, machine learning, and digital innovation in educational environments.
• Intelligent Digital Content and Cultural Computing: Exploring the intersection of artificial intelligence, digital humanities, and cultural heritage through intelligent content analysis, AI-assisted cultural preservation, computational aesthetics, and interactive digital experiences.
• Computational Aesthetics and AI Evaluation Systems: Specialized in AI-based aesthetic evaluation models, visual perception analysis, emotion-aware AI systems, and computational frameworks for measuring artistic quality and human aesthetic responses.
• Smart Interactive Media and Immersive Technologies: Research interests include intelligent interactive systems, immersive virtual environments, AI-enhanced multimedia experiences, virtual reality applications, and adaptive interactive media technologies.

The AI-Digital Content Laboratory is an advanced interdisciplinary research facility dedicated to deep learning, artificial intelligence, computer vision, natural language processing (NLP), and intelligent digital content innovation. The laboratory focuses on cutting-edge AI research, integrating machine learning, generative AI, large language models (LLMs), multimodal systems, and computational intelligence to develop next-generation intelligent technologies and creative AI applications.

Equipped with high-performance computing resources, GPU-based deep learning platforms, advanced AI software frameworks, and intelligent multimedia systems, the laboratory provides researchers with a comprehensive environment for conducting interdisciplinary AI research and developing practical AI-driven solutions. Research areas include computer vision, image segmentation, generative artificial intelligence, natural language processing, large language model applications, semantic understanding, prompt engineering, computational aesthetics, human–AI interaction, intelligent visual systems, and AI-assisted creative technologies.

The laboratory particularly specializes in natural language processing (NLP), with a strong focus on how large language models (LLMs) enhance semantic reasoning, multimodal interaction, intelligent content generation, human–AI collaboration, explainable AI systems, and AI-assisted artistic and educational applications. By combining language intelligence with visual AI technologies, the lab explores innovative approaches to computational creativity, intelligent communication systems, and human-centered AI design.

The laboratory fosters a highly collaborative and interdisciplinary research environment, bringing together experts in artificial intelligence, digital content, computer science, design, computational linguistics, and human–computer interaction to explore innovative approaches to intelligent systems and computational creativity. Through the integration of deep learning technologies, LLM-based semantic systems, and digital media innovation, the lab aims to advance the future development of AI-driven digital transformation and intelligent interactive systems.

The mission of the AI-Digital Content Laboratory is to promote pioneering research that contributes to the advancement of artificial intelligence, intelligent computing, natural language processing, and human-centered AI applications, while creating innovative solutions that respond to emerging technological and societal challenges.