Conference Information

IUI 2027: International Conference on Intelligent User Interfaces

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Submission Date:
2026-08-13
Notification Date:
2026-11-23
Conference Date:
2027-02-08
Location:
Helsinki, Finland
Years:
32
CCF: B   ICORE: A   QUALIS: A1   Viewed: 48516   Tracked: 50   Attend: 9

Call For Papers

IUI 2027 (International Conference on Intelligent User Interfaces) is a CCF B / ICORE A / QUALIS A1 conference held in Helsinki, Finland on 2027-02-08. The paper submission deadline is 2026-08-13. Acceptance notifications are sent on 2026-11-23.

Topics IUI 2027 topics of interest include, but are not limited to: 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 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 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
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Best Papers

YearBest Papers
2025Text-to-Image Generation for Vocabulary Learning Using the Keyword Method
2023Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations
2023Interacting with Next-Phrase Suggestions: How Suggestion Systems Aid and Influence the Cognitive Processes of Writing
2023ScatterShot: Interactive In-context Example Curation for Text Transformation
2022Deep Learning Uncertainty in Machine Teaching
2022Hand Gesture Recognition for an Off-the-Shelf Radar by Electromagnetic Modeling and Inversion
2022Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
2022Estimating 3D Finger Pose via 2D-3D Fingerprint Matching
2022Learning User Interface Semantics from Heterogeneous Networks with Multimodal and Positional Attributes
2019Photo sleuth: combining human expertise and face recognition to identify historical portraits
2019The effects of example-based explanations in a machine learning interface
2019When people and algorithms meet: user-reported problems in intelligent everyday applications
2019Explaining models: an empirical study of how explanations impact fairness judgment
2019The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images
2019Personalized explanations for hybrid recommender systems
2019Smell Pittsburgh: community-empowered mobile smell reporting system
2016TagFlip: Active Mobile Music Discovery with Social Tags
2016Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection

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