The ACM Conference on Intelligent User Interfaces (ACM IUI) is the annual premier venue where researchers and practitioners meet and discuss state-of-the-art advances at the intersection of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). Ideal IUI submissions should address practical HCI challenges using machine intelligence and discuss both computational and human-centric aspects of such methodologies, techniques, and systems.

This area is crucial as AI is increasingly integrated into everyday technology. Understanding and shaping AI systems for human needs is essential to ensure that AI systems are effective and responsible. As these techniques become increasingly powerful, new use cases and human-AI interactions can be explored. This conference offers an opportunity to focus the research community on important problems at the intersection of AI and HCI and bring together experts from various disciplines to discuss and build on these ideas in workshops, breaks, and networking sessions.  

Contributions are welcome from all relevant arenas, including academia, industry, government, and non-profit organizations. Diverse insights are critical to the vitality of the IUI community, and the conference will accept papers for both long and short oral presentations. Contributions to IUI are expected to be supported by rigorous evidence appropriate to the claims (e.g., user study, system evaluation, computational analysis).

Topics

IUI 2026 topics of interest include, but are not limited to:

Human-centered AI methods, approaches, and systems

Explainable AI methods
Democratization of AI
Persuasive technologies in IUI
Privacy and security of IUI
Knowledge-based approaches to user interface design and generation
User modelling for intelligent interfaces
User-adaptive interaction and personalization
IUI for crowd computing and human computation
Human control in daily automations
Trust and reliance in intelligent systems

Computational innovation

Interactive machine learning
Human-in-the loop AI testing and debugging
Human-centered recommendation and recommender systems
Generative models
Human-in-the-loop reinforcement learning
Intelligent user interfaces for generative AI

Innovative User Interfaces

Affective interfaces
Intelligent aesthetic interfaces
Intelligent collaborative interfaces
Intelligent AR/VR interfaces
Intelligent visualization and visual analytics
Intelligent wearable and mobile interfaces
Intelligent tangible interfaces

Intelligent Multimodal Systems

Embodied agents
Multimodal AI assistants
Intelligent multimodal interfaces

Intelligent Applications

Education and learning-related technologies
Healthcare and wellbeing
Automotive
Assistive technologies
Entertainment
Workplace happiness
Social media
Internet of Things (IoT)
Smart homes

Large Language Models and Agentic AI

End-user interaction with LLMs, agents, and multimodal models (e.g., chatbots, image generation)
LLMs and agents in the workplace
Human-agent interaction and multi-agent systems
Bias in LLMs and agents
The effects of LLMs and agents use on creative tasks
Personalized user interaction with LLMs and agents
Prompt engineering
User control and steering of LLMs and agents (e.g., RLHF, chaining, instruction tuning)

Evaluations of Intelligent User Interfaces

User experiments and studies
Reproducibility (including benchmarks, datasets, and challenges)
Meta-analysis
Mixed-methods evaluations