Navigation

AI and the Human in the Loop: Creation, Application, and Co-Evolution

Artificial intelligence is rapidly moving from research laboratories into real-world environments. The successful deployment of AI systems requires close interaction between algorithmic development, domain expertise, and human decision-making. URAI 2026 explores how AI technologies are created, applied, and co-evolved through human-AI collaboration — across disciplines and application domains.

The TriRhenaTech network of universities plays a crucial role in shaping AI’s responsible future. We particularly welcome submissions that align with the key research strengths of the Universities of Applied Sciences and Arts in the Upper Rhine region, members of TriRhenaTech.

Topics of Interest

We invite theoretical, technical, and applied research contributions addressing (but not limited to) the following areas:

1. AI System Development

  • Machine learning models, deep learning, and neural network architectures
  • Software engineering for AI: frameworks, tools, and best practices
  • MLOps and AI lifecycle management
  • AI-assisted software development: automated code generation, debugging, and testing
  • AI-driven software engineering processes: requirements engineering, system design, and continuous integration

2. Human-AI Interaction

  • Human-in-the-loop systems and decision support
  • Hybrid intelligence: integrating human intuition with machine learning
  • AI as a creative and collaborative assistant in design, engineering, and the arts
  • AI and the Future of Work: human-AI collaboration, new organizational models, skill transformation, and AI-supported work environments

3. AI in Application Domains

  • Healthcare & Life Sciences: AI in biotechnology, diagnostics, and patient care, digital health, and preventive healthcare systems
  • Industry & Smart Manufacturing: AI in Industry 4.0, automation, and additive manufacturing
  • Sustainability & Energy: AI for climate-neutral solutions, decarbonization, smart grids, energy-efficient systems and sustainable infrastructure

4. Responsible and Trustworthy AI

  • Transparent, explainable, and trustworthy AI
  • AI ethics, bias mitigation, and regulatory compliance
  • Cybersecurity and safety in AI-driven environments

Contributions linking AI research with societal transformation in the areas of work, sustainability, and health are particularly encouraged.

Footer